{
  "question": [
    "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?",
    "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?",
    "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?",
    "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?",
    "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?"
  ],
  "answer": [
    "Recent findings suggest that long non-coding RNAs (lncRNAs) play a significant role in the regulation of insulin secretion and sensitivity. They are involved in the regulation of islet development and function, and their abnormal expression is associated with diabetes. For instance, lncRNAs like H19, HI-LNC25, LOC283177, TUG1, MEG3, and PLUTO have been shown to influence islet structure, insulin synthesis, secretion, and glucose homeostasis. However, the exact molecular mechanisms through which lncRNAs influence pancreatic biology and diabetes are still under investigation.",
    "Post-translational modifications (PTMs) such as phosphorylation, acetylation, -Olinked N-acetylglucosamine (O-GlcNAc), SUMOylation, and ubiquitination contribute to the activation of PPAR, a key player in glucose homeostasis. These PTMs can influence ligand affinity, DNA binding, coactivator recruitment, and/or proteasomal degradation, thereby affecting the signaling pathways involved in glucose homeostasis. For instance, in the insulin signaling pathway, phosphorylation events play a crucial role in the regulation of glucose uptake and metabolism.",
    "Studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstrm Syndrome, has led to the discovery of rare, but highly penetrant, alleles that could be tracked using family-based linkage approaches. This has provided valuable insights into the cellular and molecular processes operating in the pancreatic islet and other tissues that control glucose homeostasis. Furthermore, it has uncovered a whole class of diseases, the ciliopathies, that result from defects in the genetic control of ciliary development and function.",
    "The genetic and epigenetic differences between monozygotic twins discordant for diabetes suggest that both genetic and environmental factors contribute to the disease's etiology. While monozygotic twins share 100% of their genetic material, the discordance in diabetes incidence (30-70%) indicates the influence of environmental factors. These may include diet, gestational infections, and viral infections. Epigenetic modifications, such as altered DNA methylation, also play a role in gene expression regulation and contribute to diabetes etiology. Therefore, the study of these twins helps to understand the complex interplay of genetic predisposition and environmental triggers in the development of diabetes.",
    "Recent studies have identified several therapeutic targets through the interaction between genetic variants and environmental factors in diabetes development. These include the PPARG and thiazolidinediones, KCNJ11 and sulfonylurea therapy, and the glucagon-like peptide-1 receptor (GLP1R), which is the target of GLP1R agonists. Other potential targets include drug receptors or drug metabolizers such as OCT genes, ABCC8, and CYP2C9."
  ],
  "contexts": [
    [
      "\tUsing microarray analysis, we discovered that gene-targeting of the 7 nAChR\nresults in up-regulation of an insulin-signaling network in the NAc. A genetic correlation\nnetwork of insulin-related genes and Chrna7 was independently elucidated in the NAc\nacross the BXD panel, thus validating that our microarray results are likely not due to\ndevelopmental compensation in 7 KO mice. Insulin-degrading enzyme, Ide, mRNA\nwas significantly decreased and previous rodent studies have demonstrated that both\nknock-out of this gene (Farris, 2003), as well as a mutation decreasing its catabolic\nactivity (Fakhrai, 2000), results in hyperinsulinemia and glucose intolerance.",
      "\t\n\nSome recently-identified miRNAs have been associated with insulin secretion, insulin resistance, and inflammation, and differences have emerged in some circulating miRNA levels between individuals with and without type 2 diabetes (40).Zhao and others (41) examined some miRNAs in pregnant women at 16-19 weeks of gestation (WG), finding a significantly lower expression of 3 miRNAs (miR-29a, miR-132 and miR222) in women who went on to develop GDM at 24-28 WG than in those who did not develop GDM.MiR-29 plays a part in glucose homeostasis: its overexpression inhibits insulinstimulated glucose uptake and downregulates gluconeogenesis (42).MiR-132 targets the insulin-mediated regulation of cytochrome P450 (which is involved in hepatic metabolism), and it has a role in trophoblast expansion (its reduced expression impairs normal trophoblast development) (42,43).MiR-222 is involved in regulating the cell cycle (controlling the cyclindependent kinase inhibitor).",
      "\t\n\nA human islet cis-regulatory network has been generated by integrating chromatin accessibility data, RNA-sequencing data and chromatin immunoprecipitation-sequencing data for five key -cell transcription factors (FOXA2, MAFB, NKX2.2, NKX6.1 and PDX1) 25 .Using this regulatory network, loci associated with T2DM risk that influence fasting glycaemia were found to be enriched in active islet enhancers.Furthermore, these loci were predicted to alter enhancer activity by disrupting transcription factor binding sites 25 .For example, a T2DM risk variant (rs58692659) falls within the ZFAND3 locus and was demonstrated to disrupt a NEUROD1 binding site, which is an important islet transcription factor for islet cell development and function, thereby preventing NEUROD1 binding 25 .Furthermore, T2DM risk loci are enriched in and predicted to disrupt regulatory factor X (RFX) transcription factor binding sites 27 .These data provide compelling evidence that islet-specific regulatory regions have a central role in T2DM pathophysiology and suggest a direct link between genetic variation and changes in gene expression.",
      "\t\n\nThe inability to detect insulin-signaling changes in both studies can be explained by a number of technical and biological hypotheses.First, perhaps the number of insulinsignaling genes that were transcriptionally deregulated was too few to be considered significant by statistical procedures.Second, perhaps the assembled insulin-signaling gene set used in our analysis did not accurately capture the transcriptional alterations in insulin signaling.Alternatively, it is plausible that the changes in a diabetic state were produced by phosphorylation-mediated signaling that was not detected by transcriptional profiling.",
      "\t\n\nUpon further epigenetic regulatory elements in diabetes, micro-RNAs, such as miR-15a and miR-29b, were found to be downregulated in type 2 diabetes, whereas miR-27a and miR-320a were upregulated and might open the possibility for new diagnostic markers [187, [231][232][233].",
      "\t\n\nAs ER stress markers were not activated to potentially explain reduced insulin secretion, genes related to insulin secretion pathway were investigated using real-time-PCR, which revealed downregulation of the glucose-stimulated insulin secretion (GSIS) pathway and the glucose uptake pathway in RIN-m -cells when compared to the control, indicating impairment of these pathways.mRNA levels by real-time PCR (Fig. 4c) showed a decrease in glucose transporter 2 (Glut2 [MIM: 138160]) to 54% compared to the control, p < 0.001.Pancreatic and duodenal homeobox 1 (Pdx1 [MIM: 600733]) was also suppressed to 85.7%, p = 0.01.On the other hand, the forkhead box protein A2 (Foxa2 [MIM: 600288]) mRNA level, which regulates PDX1, was unchanged, while the mRNA of glucokinase (Gck [MIM: 138079]), which phosphorylates glucose in the first step of the GSIS pathway in -cells, was slightly elevated (11.5%, p = 0.008).",
      "\t\n\nIt has been hypothesized that most of the new genetic variants affect -cell function, development or survival but not insulin sensitivity [6].Consistent with this, we found all of the genes except Adam30 and Cdkn2a were expressed in pancreatic islets.These genes were expressed, however in the transformed -cell line, MIN6.The expression of all the genes except Lgr5 decreased following incubation of the islets in high glucose concentrations.It can thus be hypothesized that these genes may normally play a beneficial role in islet function, and a reduction in the expression of these genes could contribute to glucotoxic -cell dysfunction or survival.However, we also found evidence that most of the genes could have potential roles in other metabolically-relevant tissues.Genes affecting insulin sensitivity may be expected to be expressed in peripheral insulin sensitive tissues, such as liver and adipose tissue, and be responsive to metabolic status.Consumption of a high fat diet was associated with a tendency for the expression of several of these genes to be decreased.Similarly, many of the genes were regulated by feeding and fasting.Only the two splice isoforms of Cdkn2a had no evidence of metabolic regulation in any of the other tissues examined.",
      "\t\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p)  have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\t\n\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p)  have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\tDiscussion\n\nUsing next-generation sequencing, we have established the first catalog of miRNAs in human pancreatic islets and beta-cells, and explored the overlap between these miRNAs and T2D genetic susceptibility.Our catalog not only serves as a valuable resource for those interested in the roles of specific miRNAs in normal islet physiology and beta-cell function, it also provides a reference for the study of miRNA mediated abnormalities in islets from type 2 diabetic donors.The abundance of miR-375 in the miRNA profile provides valuable support for a critical role in human pancreatic beta-cells, mirroring the well-established role in rodent islet biology.miR-375 null mice are hyperglycaemic and exhibit reduced beta-cell mass [40].In a clonal rodent beta-cell line (MIN6), knockdown or over-expression of this miRNA influences glucose-stimulated insulin secretion [7].Furthermore, knockdown of miR-375 in obese ob/ ob mice results in a more profound effect on glycaemia leading to a severe diabetic phenotype in these mice [40].Our study establishes that miR-375 is also abundantly expressed in human islets and warrants further studies to define the contribution of miR-375 to the pathogenesis of T2D.\t\n\nFew of the 10 most islet-specific miRNAs (Figure 2B; all with specificity scores .0.8) have previously been implicated in islet function.For miR-184, miR-182-5p and miR-127-3p, there is published evidence for a role in insulin biosynthesis and secretion, though for miR-184 and miR-127-3p this is restricted to a correlation between islet expression levels and glucose-stimulated insulin secretion [17,18].For other miRNA transcripts, such as miR-409-5p and miR-183-5p, the high degree of islet-specificity may point to novel roles in the development and maintenance of islet cellular phenotype.",
      "\t\n\nIn sum, this work provides new information about how CDKN2A/B T2D SNPs impact islet biology, suggests that the ANRIL lncRNA may play a role in human islets, and uncovers a link between a T2D SNP and b-cell proliferation.Further studies into the CDKN2A/B locus to develop a mechanistic understanding of how these SNPs impact islet biology to influence T2D risk could one day open the door for using personalized genomic information to inform T2D subtype definitions and therapeutic choice.",
      "\t\n\nThe following section will discuss the roles of lncRNAs in metabolic tissues and deregulation of which are implicated in varied metabolic phenotypes associated with diabetes.\tLncRNAs as regulators of islet function\n\nThe pancreatic islet is an important central node to researchers to understand the pathophysiology of diabetes [53].The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding et al., where the lncRNA, H19 (Fig. 4), was shown to be involved in transgenerational transmission of gestational diabetes mellitus which leads to impaired islet structure and function [54].To understand the roles of lncRNAs in regulating pancreatic function, several research groups have profiled lncRNA expression in mouse and human pancreatic islets [55,56].Transcriptome analysis in pancreatic -cells of type 2 diabetes patients identified tissue-specific and dynamically regulated abnormally expressed lncR-NAs.These lncRNAs are often located near islet-specific chromatin domains containing islet-specific coding genes or mapped to diabetes susceptible genetic loci.Knockdown of HI-LNC25, a  cell-specific lncRNA conserved between mouse and human resulted in decreased GLIS3, an important islet transcription factor, thereby suggesting its functional importance in pancreatic  cells [56] (Fig. 4).A coexpression analysis has identified that the lncRNA, LOC283177, correlates with the expression of insulin synthesis and secretion [51] (Fig. 4).Yin et al. demonstrated that silencing of the lncRNA, TUG1 in vivo increased apoptosis in pancreatic  cells and decreased insulin secretion leading to elevated fasting glucose levels (Fig. 4).Expression of TUG1 is decreased in a non-obese diabetic (NOD) mouse and is suppressed by glucose treatment in pancreatic Nit-1 cells, indicating its association with diabetes [57].Another lncRNA, MEG3 was reported to be downregulated in the pancreatic tissue of Type 1 Diabetic (T1D) and T2D mice models and its expression was dynamically modulated by glucose in Min6 and primary mouse islet cells (Fig. 4).In vivo silencing of MEG3 led to impaired glucose tolerance and decreased insulin secretion, as also evident by the reduced insulin-positive cells.There was a significant decrease in the Pdx-1 and MafA levels indicating MEG3 as a novel -cell regulator [58].Deletion of a conserved lncRNA, linc1 (-cell long intergenic non-coding RNA 1) in adult mice results in defective islet development and disruption of glucose homeostasis [59] (Fig. 4).Decreased levels of the lncRNA, PLUTO (Fig. 4) in islets of T2D or impaired glucose tolerant subjects affect the 3D chromatin structure and transcription of Pdx-1, a key  cell transcription factor implicating its role in insulin synthesis and  cell-specific regulatory network [60].In spite of these reports, the elucidation of lncRNAmediated molecular mechanisms in pancreatic biology still awaits further detailed investigations.",
      "\t\n\nThe known tissue specificity of gene expression regulation means that the most informative studies will measure transcript levels in the specific tissue(s) relevant to the disease.In the case of type 2 diabetes, characterization of physiological responses (e.g., stimulus-induced insulin secretion, insulin sensitivity) suggests most loci are associated with defects in pancreatic b-cell function (2,3,7).Therefore there is a real need to measure gene expression in human b-cells (or whole islets, as these have been shown to be a suitable proxy [8]).There have, however, been very few reports linking type 2 diabetesassociated variation with islet gene expression using the classical eQTL approach (9,10).",
      "\tInsulin secretion\n\nProgression from altered glucose metabolism to overt diabetes occurs as the reduction in -cell mass and function is further aggravated.Thus, an attractive intervention is one that will halt the progressive decline in -cell mass and function and prevent the need for exogenous insulin replacement that otherwise follows 1 .Agents that suppress inflammation, including IL-1 blockers and salsalate (a potent inhibitor of NF-B), have shown some promise in improving glycaemic control and -cell function 143,269,270 .MicroRNAs play a pivotal part in the physiological and pathological processes involved in glucose metabolism by post-transcriptional regulation of gene expression.Particular microRNAs can regulate -cell function 271 , exposing key regulatory signalling pathways involved in restoration of -cell mass, and provide a promising strategy for improving insulin secretion and -cell health in T2DM.Identification of novel insulin secretagogues that act directly on -cells and enteroendocrine K cells and L cells in the intestine are under investigation, and members of the G protein-coupled class of receptors have shown promise 272 .GLP1 receptor agonists induce -cell proliferation in rodents 273 , but studies in humans have not demonstrated a similar effect 237 .A series of novel signalling pathways have been reported to be strongly associated with -cell mass restoration.For example, the PI3K-PKC pathway has been shown to augment glucose-mediated -cell prolifer ation, and activation of PKC may provide a novel approach to increase human -cell proliferation 274 .",
      "\t\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.\tGWAS-Identified Genes\n\nFollowing the successful identification of genetic loci by GWAS, several candidate genes within or surrounding genetic loci which are thought to play roles in b cell function, in particular, in proinsulin processing and secretion, have been examined in mechanistic studies.Gene editing tools have quickly replaced techniques such as shRNA-based silencing and HDR-mediated deletion to become a mainstream technique in studies of gene function.For example, the critical b cell-enriched NEUROD1 and SLC30A8 genes were deleted in EndoC-bH1 cells using these approaches in recent studies (243).Similarly, pancreatic duodenum homeobox-1 (PDX1), an important regulator of the INS gene, was also mutated by CRISPR-Cas9 resulting in a line with defective glucose-induced Ca 2+ influx and insulin secretion (244).Our laboratory has inactivated the type 2 diabetes-related STARD10 and FCHSD2 genes in EndoC-bH1 cells using a lentiviral approach and demonstrated effects on insulin secretion (and see above) (117).Furthermore, Fang et al. used CRISPR screening technology and identified several genes involved in insulin regulation in mouse MIN6 cells (172).\t\n\ninsulin secretion.We begin by providing examples of genes and loci associated with altered T2D risk.Finally, we review the CRISPR tools that may offer the potential to correct these variants in the human b cell.",
      "\t\n\nT2D loci were also identified at clusters of noncoding RNAs with roles in islet  cell function.One locus includes a set of microRNAs specifically expressed in islet  cells, the maternally expressed noncoding RNA MEG3, and the paternally expressed gene DLK1.Targets of these microRNAs increase  cell apoptosis 40 , and reduced Meg3 expression impairs insulin secretion 41 .DLK1 inhibits adipocyte differentiation, thereby protecting against obesity 3 , and promotes pancreatic ductal cell differentiation into  cells, increasing insulin secretion 42,43 .Other variants near MEG3 have been associated with type 1 diabetes 44 (EAS and EUR LD r 2 = 0 with EAS lead variant).The other noncoding RNA locus is the MIR17HG cluster of miRNAs, which regulate glucose-stimulated insulin secretion and pancreatic  cell proliferation stress 45 ; one of these microRNAs, miR-19a, affects hepatic gluconeogenesis 46 .Yet another T2D locus is located near TRAF3, which is a direct target of the MIR17HG microRNA cluster and promotes hyperglycaemia by increasing hepatic glucose production 47,48 .The T2D association results suggest that these noncoding RNAs influence disease susceptibility."
    ],
    [
      "\tThis\nphosphorylation triggers the activation of the docking protein IR substrate 1 (IRS1), which\nsubsequently activates phosphatidylinositol 3-kinase (PI3K) and RAC serine/threonineprotein kinase 2 (AKT2), which has a critical role in glucose metabolism. PI3K and AKT2\nactivation promotes the translocation of glucose transporter 4 (GLUT4) and the free fatty\nacid (FFA) transporter CD36 from intracellular stores to the plasma membrane, thereby\n\nNat Rev Cardiol. Author manuscript; available in PMC 2021 February 01. Tan et al. Page 48\n\nAuthor Manuscript\nAuthor Manuscript\n\nleading to increased glucose and FFA uptake.",
      "\tProtein kinase B (c-Akt) in phosphatidylinositol-3-OH\nkinase\nsignal\ntransduction. Nature. 1995;376(6541):599-602.\ndoi:10.1038/376599a0\n\n53. Herzig S, Long F, Jhala US, et al. CREB regulates hepatic gluconeogenesis\nthrough\nthe\ncoactivator\nPGC-1. Nature. 2001;413(6852):179-183.\ndoi:10.1038/35093131\n\n54. Matsumoto M, Pocai A, Rossetti L, Depinho RA, Accili D. Impaired regulation of\nhepatic glucose production in mice lacking the forkhead transcription factor\nFoxo1 in liver. Cell Metab. 2007;6(3):208-216. doi:10.1016/j.cmet.2007.08.006\n\n55. Wang ND, Finegold MJ, Bradley A, et al. Impaired energy homeostasis in\nC/EBP alpha knockout mice. Science. 1995;269(5227):1108-1112.\ndoi:10.1126/science.7652557\n\n56.\tIt exerts its functions through\n\nactivating the phosphatidylinositol-3-kinase (PI3K)-AKT signaling pathway and\nphosphorylating a variety of substrates, including glycogen synthase kinase-3 (GSK3)\n51\n\n, the forkhead (FOXO) transcription factors, and cAMP regulatory element-binding\n\nprotein (CREB)\n\n52\n\ngluconeogenesis\n\n. CREB, FOXO1, and C/EBP are transcription factors involved in\n\n5355\n\n. The detailed mechanisms of how insulin maintains albumin\n\nexpression require further investigation. Insulin resistance occurs in patients with sepsis\n\n56\n\n, obesity and diabetes\n\n57\n\n, implying\n\na role for severe or persistent inflammation.",
      "\t\n\n) including PABPC4, NRBP1, CALCRL, CTC-498M16.4,and FADS1.Shared TWAS associations suggested the involvement of glucose and energy homeostasis via PKB/AKT signaling or epigenetic modulator (methylation, acetylation, or lncRNA) in RHR and T2D/cardiometabolic, and provided potential biological shared pathways, mechanisms, or potential therapeutic targets to follow-up in the future.",
      "\t\n\nThe above discussion remarkably converges on the TGF-beta signaling effector SMAD3.TGF-beta signaling is involved in the regulation of insulin gene transcription, pancreatic islets b cell function, and glucose tolerance and energy homeostasis [36,[59][60][61].SMAD3 is known to localize at insulin gene promoter and repress insulin gene transcription [61].SMAD3 knock-out mice are associated with improved glucose tolerance and insulin sensitivity [36].Exhibiting altered expression of genes related to adipogenesis, lipid accumulation, and fatty acid b oxidation, these mice show resistance to obesity and insulin resistance induced by high fat diet [36,59].Further, levels of TGF-beta1 have been found to positively correlate with adiposity in human subjects [59].Also, systemic blockade of TGF-beta signaling has been found to protect mice from obesity, diabetes and hepatic steatosis [59].Indeed, pharmacological manipulation of TGF-beta signaling is considered to offer a potential therapeutic strategy in obesity and diabetes [59,60].",
      "\tSignal transduction\n\nMAPK1 is an important regulator of -cell function (Lawrence et al, 2008), for example contributing directly to short-versus long-term insulin response and regulation of pro-apoptotic CHOP10 (Lawrence et al, 2007).MAPK1 constitutes the center of a regulatory network implicated in elevated free fatty acid (FFA) levels (Sengupta et al, 2009) common in T2D patients.MAPK/ERK signalling is exacerbated by FFA that lead to dephosphorylation of cascade proteins by PP2A/PPP2R4 (Guo et al, 2010) pointing towards a certain level of interwovenness between the identified processes, in this case signal transduction (adaptation category) and ER stress (dysfunction/cell death category, cf. Figure 5B). (Figure 2C) CDK5R1 acts as an activator of CDK5 (Ubeda et al, 2004) whose expression is regulated by glucose and which inhibits insulin secretion (Wei et al, 2005).Hyperglycaemia-caused overactivation of CDK5 may contribute to -cell glucotoxicity (Ubeda et al, 2006). (Figure S4C)",
      "\tThe binding of insulin with its ligand specific\nreceptor increases glucose metabolism, lipid synthesis\nand cellular proliferation via PKB/AKT signaling [27, 28]. In fact, dysregulation of PKB/AKT signaling provokes a\nbroad range of diseases such as cancer, diabetes and heart\ndisease [29, 30]. CTMP was first identified as a PKB/AKT\nbinding partner with tumor-suppressor function. PKB/\nAKT is negatively regulated by the binding of CTMP\nwith the C-terminal regulatory domain of pPKB/AKT\n[31, 32]. Together with CTMP, LETM1 is associated with\nmitochondrial morphology via optic atrophy 1 (OPA1)\nregulation [33].",
      "\t\n\nWith T2D status, and with increases in fasting glucose, fasting insulin and BMI, we observed lower expression of genes involved in endoplasmic reticulum protein localization and translational elongation.For T2D, the most significant trends were for decreased expression of cellular respiration genes (q-value  1.4  10  35 ), consistent with previous observations in skeletal muscle samples from T2D and NGT individuals following hyperinsulinemic-euglycemic clamp 7 .Mitochondrial regulatory protein PGC-1alpha (PPARGC1A) was identified by Mootha et al. 7 as a potential master regulator of mitochondrial expression.We observed lower, non-significantly different expression levels of PPARGC1A (b   0.24, q-value  0.57) in individuals with T2D.Decreased mitochondrial function is a component of the mTOR pathway which is dysregulated in metabolic diseases; downregulation of the pathway shifts cells away from protein synthesis and cell growth and towards protein catabolism 8 .Consistent with this, for T2D, we observed lower expression of genes involved in generation of precursor metabolites, translational elongation and higher expression of genes involved in protein polyubiquitination (Fig. 1c).",
      "\t\n\nTwo negative feedback loops in this insulin signaling pathway are of interest.Additionally to tyrosine phosphorylation, both the insulin receptor and IRS proteins are also phosphorylated on serine residues, which may attenuate ) inhibition under certain conditions described in the review; green: insulinomimetic effects of zinc; red: effects of zinc deficiency leading to insulin resistance.After binding of insulin to the  subunits of the tetrameric insulin receptor, the kinase activity of the  subunit is stimulated, which results in transphosphorylation of the  subunit [35,130].This induces phosphorylation of members of the IRS family and subsequent interaction with signaling molecules like the p85 subunit of the PI3K [131].PI3K in turn triggers phosphorylation of PDK1, a serine kinase that activates Akt/PKB [132,133].Akt leads to stimulation of GLUT 4 translocation in adipocytes and to inhibition of GSK-3, thereby allowing activation of glycogen synthase in adipocytes, translocation of GLUT to the cell surface and induction of glucose metabolism [35,[134][135][136][137][138][139][140][141].In addition, inhibition of GSK-3 results in enhanced protein synthesis and gene expression [35,142].Zinc leads to tyrosine phosphorylation of the  subunit of the insulin-receptor [143 a ] and to inhibition of PTP1B which dephosphorylates the insulin receptor, thus increasing phosphorylation of the receptor [144 b ].Akt is activated by zinc in a PI3K-dependent way [143 c ] and zinc inhibits GSK-3, just like insulin [145 d ].Moreover, zinc plays a role in glucose transport since it is part of IRAP, a molecule probably required for maintenance of normal GLUT levels [129 e ].Zn: zinc.\t\n\nsignaling by decreasing insulin-stimulated tyrosine phosphorylation.This is mediated by PI3K, Akt, GSK-3 and mammalian target of rapamycin [35].GSK-3 is capable of phosphorylating IRS-1, subsequently converting this molecule into an inhibitor of the insulin receptor tyrosine kinase activity in vitro and in insulin-resistant rat muscle after insulin stimulation [141,158].A second mechanism negatively influencing insulin signaling is the rapid dephosphorylation of the insulin receptor and its substrates by protein tyrosine phosphatase 1B (PTP1B) [35].",
      "\tDiscussion\n\nThe G protein/cAMP/PKA mediated signal transduction pathway is of high importance for growth, cell differentiation and metabolism due to extracellular ligands.The a-subunit of stimulatory G proteins Gsa is crucial for mediating these effects.In the present study, we report the positive results of the largest mutation screening of the a subunit of stimulatory G proteins described so far, leading to the identification of two new hotspots and 33 mutations that have not been reported before.Furthermore, we demonstrate for the first time a connection between the severity of the mutation and the phenotypical signs of subcutaneous calcifications and brachymetacarpia in patients with PHPIa.",
      "\t\n\nFigure 1: Schematic representation of the insulin-signaling pathway.Dashed light-blue line borders indicate insulin-signaling inhibitor proteins.PTPRF = protein tyrosine phosphatase receptor type F; ENPP1 = ectonucleotide pyrophosphatase/phosphodiesterase 1; PTPN1 = protein tyrosine phosphatase nonreceptor type 1; IRS = insulin receptor substrate; PI3K = phosphoinositides 3 kinase; nck = noncatalytic region of tyrosine kinase adaptor protein 1; INPPL1 = inositol polyphosphate phosphatase-like 1; TRIB3 = tribbles homolog 3; mTOR = mammalian target of rapamycin; Foxo = forkhead box protein O1; BAD = Bcl-2-associated death promoter; PHAS-I = phosphorylated heatand acid-stable protein regulated by insulin; and p70S6K = p70-ribosomal S6 kinase.",
      "\t\n\nand although complex, occur largely in a canonical sequence resulting in a single outcome (Fig. 2) -hence perturbation at any stage in this sequence will almost inevitably result in decreased release of the hormone into the portal circulation.In contrast, variations in function of a single gene product involved in insulin signalling are unlikely to have an effect on all aspects of insulin action and hence would not present with major effects on glucose metabolisms (Fig. 2).",
      "\t\n\nUnder normal conditions, the glucose regulation process commences when insulin binds to its corresponding insulin receptor (IR), which results in auto-phosphorylation of its tyrosine residues [171].This allows IR to phosphorylate insulin receptor substrate 1 (IRS-1) on tyrosine residues, which further triggers the phosphorylation of downstream molecules and induces the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade [171,172].PI3K, when activated, results in the conversion of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).Consequently, downstream 3-phosphoinositide dependent protein kinase1 (PDK1) is activated, which subsequently activates, among other kinases, Akt, resulting in phosphorylation of its substrate (AS160), which regulates translocation of glucose transporter 4 (GLUT4) to the transmembrane and allows for glucose uptake and regulation of protein and lipid metabolism [171,172] (Figure 4).\t\n\nUnder normal conditions, the glucose regulation process commences when insulin binds to its corresponding insulin receptor (IR), which results in auto-phosphorylation of its tyrosine residues [171].This allows IR to phosphorylate insulin receptor substrate 1 (IRS-1) on tyrosine residues, which further triggers the phosphorylation of downstream molecules and induces the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade [171,172].PI3K, when activated, results in the conversion of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).Consequently, downstream 3-phosphoinositide dependent protein kinase1 (PDK1) is activated, which subsequently activates, among other kinases, Akt, resulting in phosphorylation of its substrate (AS160), which regulates translocation of glucose transporter 4 (GLUT4) to the transmembrane and allows for glucose uptake and regulation of protein and lipid metabolism [171,172] (Figure 4).Insulin binds to the insulin receptor, causing autophosphorylation of its tyrosine residues.This causes phosphorylation of insulin receptor substrate-1 (IRS-1) on its tyrosine residues, which leads to the phosphorylation of the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade.PI3K catalyzes the phosphorylation of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).PIP3 activates 3-phosphoinositide-dependent protein kinase-1 (PDK-1) as a result, which in turn, phosphorylates the downstream protein \"AKT\", which phosphorylates its substrate AS160.AS160 regulates glucose translocator 4 (GLUT4) and aids in its translocation to the plasma membrane, where it allows glucose to flow.\t\n\nFigure 4. PI3K/Akt signaling pathway.Insulin binds to the insulin receptor, causing autophosphorylation of its tyrosine residues.This causes phosphorylation of insulin receptor substrate-1 (IRS-1) on its tyrosine residues, which leads to the phosphorylation of the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade.PI3K catalyzes the phosphorylation of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).PIP3 activates 3-phosphoinositide-dependent protein kinase-1 (PDK-1) as a result, which in turn, phosphorylates the downstream protein \"AKT\", which phosphorylates its substrate AS160.AS160 regulates glucose translocator 4 (GLUT4) and aids in its translocation to the plasma membrane, where it allows glucose to flow.\t\n\nIn GDM pregnancies, decreased expression levels of the following insulin signaling components: IRS1, PIP3, PIK3, and GLUT4, have been reported [173][174][175].Furthermore, alternative phosphorylation of IRS1 at serine residues was exhibited in GDM patients, which prevents the PI3K signaling cascade from taking place, and thus, inhibits insulin action [176].The exact underlying mechanism through which disrupted insulin signaling Insulin binds to the insulin receptor, causing autophosphorylation of its tyrosine residues.This causes phosphorylation of insulin receptor substrate-1 (IRS-1) on tyrosine residues, which leads to the phosphorylation of the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade.PI3K catalyzes the phosphorylation of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).PIP3 activates 3-phosphoinositide-dependent protein kinase-1 (PDK-1) as a result, which in turn, phosphorylates the downstream protein \"AKT\", which phosphorylates its substrate AS160.AS160 regulates glucose translocator 4 (GLUT4) and aids in its translocation to the plasma membrane, where it allows glucose to flow.",
      "\tIn conclusion, by employing a combination of pharmacological and genetic gain- and loss-of-function genetic approaches,\nour studies show that the activation of the TGR5 signaling\npathway counteracts the metabolic dysfunction associated\nwith diabesity. TGR5 activation results in a range of beneficial\nmetabolic effects that include resistance to weight gain and\nhepatic steatosis, preservation of liver and pancreatic function,\nand the maintenance of glucose homeostasis and insulin sensitivity. These effects are due to enhanced mitochondrial function\nin muscle, BAT, and enteroendocrine cells, resulting in an\nincrease in energy expenditure and incretin secretion (Figure 7).",
      "\tInsulin and DHEA signaling\n\nIn addition to the changes in central metabolic pathways, we found significant regulation of hormonal pathways.We could reproduce the transcriptional regulation of IGFs (insulin-like growth factors) and IGFBPs (IGF binding proteins).IGF1 is a major growth signaling molecule that is transcriptionally activated by insulin and growth hormone (GH) under good nutrient conditions, thereby allowing cell growth and proliferation (Kelley et al., 1996)  sion is strongly reduced, while its deactivating binding proteins IGFBP1 and IGFBP2 are up-regulated.",
      "\tPost-Translational Modifications Control PPAR Signaling Affecting Drug Effectiveness\n\nDistinct biological networks converge into PPAR signaling and several molecular effectors directly or indirectly regulate its activation [19], resulting in finely regulated tissue-specific responses.A large number of endogenous/exogenous compounds, coactivators, and corepressors affect PPAR activity, inducing different signal transduction pathways and biological effects.Beyond epigenetic, transcriptional, and translational regulatory mechanisms, different post-translational modifications (PTMs), such as phosphorylation, acetylation, -Olinked N-acetylglucosamine (O-GlcNAc), SUMOylation, and ubiquitination, contribute to PPAR activation [120].Each PTM represents a separate feature to be exploited for cell-or tissue-specific modulation [17], allowing rapid responses to internal and external stimuli.Of note, PTMs control PPAR activity, potentially influencing ligand affinity, DNA binding, coactivator recruitment, and/or proteasomal degradation."
    ],
    [
      "\tA GLIMPSE INTO THE FUTURE\n\nGetting from the extremes to a comprehensive view of diabetes genetics.As described above, success in the identification of genes impacting on individual risk of diabetes has come from two distinct approaches to gene discovery.The first, linkage mapping within monogenic and syndromic families, has delivered causal variants that are rare but highly penetrant.The second, large-scale association mapping, is now yielding growing numbers of common variants: these have, at best, modest effect sizes and low penetrance.Several genes are featured in the lists generated by both approaches.For example, mutations in KCNJ11, PPARG, WFS1, and TCF2 (HNF1B) are causal for syndromic and/or monogenic forms of diabetes, while common variants in these same genes influence predisposition to typical type 2 diabetes (55,56,64 -66).While common variants in GCK (another gene causal for MODY) do not influence type 2 diabetes risk per se, they have a clear impact on fasting glucose levels within the population (88).\tLESSONS LEARNED FOR MULTIFACTORIAL DISEASE\n\nMonogenic and syndromic forms account for only a small, though highly informative, proportion of cases of nonautoimmune diabetes.The challenge for medical science lies in bringing equivalent mechanistic insights and translational benefits to the hundreds of millions of people already affected by, or at risk of, more common, typical forms of diabetes.For type 2 diabetes, there is abundant evidence that individual susceptibility is influenced by both the combination of genetic variation at multiple sites and a series of environmental exposures encountered during life (52).Tracking down the specific genetic variants involved has been tougher than for monogenic forms of disease, since the correlations between genotype and phenotype are far weaker (53,54).However, recent efforts have now identified at least 17 confirmed type 2 diabetessusceptibility variants (  (69), and development and exploitation of this methodology has had the greatest impact on susceptibility gene discovery.Even so, many of these discoveries have been hard-won.One reason for this is that the \"candidate\" gene-based approach has proved, with notable exceptions (55,56), to be an inefficient route to susceptibility gene discovery; it is only with the advent of functionally agnostic genome-wide approaches that the floodgates have opened (70).Another reason is that detection of the variants of modest effect that appear to be responsible for much of type 2 diabetes susceptibility (per-allele odds ratios [ORs] 1.10 -1.40, for risk-allele frequencies 10 -90%) has required association studies conducted in extremely large sample sizes (thousands of individuals) (54).Variants within TCF7L2 have the largest effects seen so far, with a per-allele OR of 1.4 (57): the 15% of Europeans carrying two copies of the risk allele are at approximately twice the lifetime risk of type 2 diabetes as the 40% who have none.",
      "\tLessons from GWA studies\n\nThe most important lesson is the demonstration of the power of genetics to provide novel insights into disease aetiology.Of the 11 genes or regions now implicated in type 2 diabetes, only four were strong biological candidates (PPARG, KCNJ11, WFS1, TCF2) [8,9,[11][12][13][14].Three had some corroborating evidence (IGF2BP2, the HHEX-IDE gene region, SLC30A8) [2][3][4][5][6], but for the remainder, evidence of their link to diabetes came as a complete surprise.These studies provide the first evidence implicating Wnt-signalling pathways (TCF7L2) and cell cycle control (CDKAL1 and CDKN2A/2B) in the pathogenesis of type 2 diabetes [2,3,5,6].For type 1, the key new discoveries highlight the contribution to disease pathogenesis of the PTPN gene family and IL-2 signalling [1,7].",
      "\t\n\nMajor consortia addressing the genetic basis of diabetes complications and associated traits",
      "\t\n\nGenetic determinants of diabetes and metabolic syndromes.",
      "\t\n\nUnfortunately, these questions are not yet answered.The early 1990s was the beginning of the era of molecular biol- ogy, and it was generally assumed that within a few years this powerful new technology would identify the genetic defects in type 2 diabetes.Indeed, the genetic basis for many monogenic forms of diabetes has been discovered such as mitochondrial genome defects and the association with diabetes and deafness, Wolfram's syndrome, several rare syndromes of extreme insulin resistance and obesity, and many of the MODY syndromes (maturity onset diabetes of youth).Still, these account for only a small proportion of diabetes.",
      "\t\n\nGenome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).",
      "\t\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.\t\n\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.",
      "\t\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.",
      "\t\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.",
      "\t\n\nIn the past decade, genome-wide association (GWAS) and sequencing studies have identified genetic loci that help explain the inherited basis of T2D and glycemic traits.These studies are providing insights into the genetic architecture of T2D, including the number, frequency and effect sizes of risk variants in populations around the world.The polygenic nature of T2D is now well established, and multiple risk variants are being identified at some loci, suggesting allelic heterogeneity.Concurrently, increasing numbers of genes and variants have been implicated in monogenic forms of diabetes, including maturity onset diabetes of the young (MODY) and neonatal diabetes (7), and at least five genes have been implicated in both monogenic and polygenic diabetes (8).A recent simulation study evaluated genetic architectures for consistency with results from T2D genetic studies and found that many different disease models were still possible with respect to the number of loci, allele frequencies and level of selective pressure (9).Ongoing studies should more substantially narrow the bounds on feasible architectures (9).",
      "\t\n\nIn the case of relatively uncommon monogenic and syndromic forms of diabetes, such as maturity onset diabetes of the young (MODY) and neonatal diabetes, identification of rare causal mutations has delivered both knowledge and clinical translation [4,5].In contrast, progress in unravelling the genetic architecture of more typical, common, multifactorial type 2 diabetes has been painfully slow [6].The reasons have been well-rehearsed [7].The complex web of susceptibility factors-genetic, environmental, social-that contributes to individual risk of developing type 2 diabetes means that most predisposing genetic variants will have only a modest marginal impact on disease risk.The majority of genetic studies performed to date have simply had insufficient power to uncover these reliably [7].The few type 2 diabetes-susceptibility variants convincingly demonstrated-notably the P12A variant in PPARG and E23K in KCNJ11 [8,9]-have only modest effects on disease risk (odds ratios ~1.2), far too small to offer (either individually or in combination) clinically useful predictive testing.Since these variants lie within genes whose products are already known to be therapeutic targets, these particular discoveries have also had limited capacity to deliver novel pathophysiological insights.Among those working on the genetics of type 2 diabetes, there was growing apprehension that these two genes might be providing a representative view of the genetic architecture of type 2 diabetes.",
      "\t\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.",
      "\t\nWhilst the heritable nature of Type 2 diabetes has been recognized for many years, only in the past two decades have linkage analyses in families and genome-wide association studies in large populations begun to reveal the genetic landscape of the disease in detail.Whilst the former have provided a powerful means of identifying the genes responsible for monogenic forms of the disease, the latter highlight relatively large genomic regions.These often harbour multiple genes, whose relative contribution to exaggerated disease risk is uncertain.In the present study, the approaches that have been used to dissect the role of just a few (TCF7L2, SLC30A8, ADCY5, MTNR1B and CDKAL1) of the ~500 genes identified at dozens of implicated loci are described.These are usually selected based on the strength of their effect on disease risk, and predictions as to their likely biological role.Direct determination of the effects of identified polymorphisms on gene expression in disease-relevant tissues, notably the pancreatic islet, are then performed to identify genes whose expression is affected by a particular polymorphism.Subsequent functional analyses then involve perturbing gene expression in vitro in b-cell lines or isolated islets and in vivo in animal models.Although the majority of polymorphisms affect insulin production rather than action, and mainly affect the b cell, effects via other tissues may also contribute, requiring careful consideration in the design and interpretation of experiments in model systems.These considerations illustrate the scale of the task needed to exploit genome-wide association study data for the development of new therapeutic strategies.\t\n\nWhilst the heritable nature of Type 2 diabetes has been recognized for many years, only in the past two decades have linkage analyses in families and genome-wide association studies in large populations begun to reveal the genetic landscape of the disease in detail.Whilst the former have provided a powerful means of identifying the genes responsible for monogenic forms of the disease, the latter highlight relatively large genomic regions.These often harbour multiple genes, whose relative contribution to exaggerated disease risk is uncertain.In the present study, the approaches that have been used to dissect the role of just a few (TCF7L2, SLC30A8, ADCY5, MTNR1B and CDKAL1) of the ~500 genes identified at dozens of implicated loci are described.These are usually selected based on the strength of their effect on disease risk, and predictions as to their likely biological role.Direct determination of the effects of identified polymorphisms on gene expression in disease-relevant tissues, notably the pancreatic islet, are then performed to identify genes whose expression is affected by a particular polymorphism.Subsequent functional analyses then involve perturbing gene expression in vitro in b-cell lines or isolated islets and in vivo in animal models.Although the majority of polymorphisms affect insulin production rather than action, and mainly affect the b cell, effects via other tissues may also contribute, requiring careful consideration in the design and interpretation of experiments in model systems.These considerations illustrate the scale of the task needed to exploit genome-wide association study data for the development of new therapeutic strategies.",
      "\tA\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12).",
      "\t\n\nThe earliest successes for genetic discovery in diabetes and obesity arose from the study of monogenic and syndromic forms of disease, for which the segregation of rare, but highly penetrant, alleles could be tracked using family-based linkage approaches that are well suited to that setting.Maturity-onset diabetes of the young, for example, accounts for ~1-2% of cases of nonautoimmune diabetes presenting in early adulthood. 14ost cases of maturity-onset diabetes of the young are now known to result from rare coding mutations in either the hepatocyte nuclear factor-1A (HNF1A) or glucokinase (GCK) genes.In patients with these conditions, a precise molecular diagnosis brings important benefits in terms of individual prognostication and treatment optimization. 14These discoveries have also generated valuable insights into the cellular and molecular processes-operating in the pancreatic islet and other tissuesthat control glucose homeostasis. 15To give a further example, identification of the mutations underlying syndromic forms of obesity, including Bardet-Biedl, has uncovered a whole class of diseases, the ciliopathies, that result from defects in the genetic control of ciliary development and function. 16arly attempts to apply family-based linkage approaches to more common forms of diabetes and obesity proved to be unrewarding.In their seminal paper in 1996, Risch and Merikangas 17 highlighted the merits of association, as opposed to linkage, analysis for the detection of the low-penetrance alleles most likely to be relevant to common disease.It would take a decade before the density of available markers would allow genomewide screens for association to be implemented. 18In the interim, association analyses that focused attention on genetic variation within presumed biological candidates resulted in some successes in risk variant detection.For T2D, these included associations with variants in the genes encoding key therapeutic targets such as the peroxisome proliferator-activated receptor- (PPARG) and the islet K ATP channel (KCNJ11); 19,20 an equivalent example for obesity would relate to variants in the melanocortin 4 receptor (MC4R) gene. 21More often than not, however, these candidate gene studies were plagued by inadequate sample size and overly liberal significance thresholds, a lethal combination that led to a profusion of unreliable reports of association. 22][25][26] Given the content of the genotyping arrays employed, these studies have focused on the detection of signals attributable to common variants (typically of a minor allele frequency above 5%).9][30][31] In the case of T2D, the current count of risk loci, each confirmed to genome-wide significance, is around 65; [27][28][29] for BMI and obesity, the count is about half that number. 25Looking across these loci, several important features emerge.",
      "\t\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.",
      "\t\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?"
    ],
    [
      "\tGenes\n\n2][43][44][45][46][47] Twin studies need to be considered carefully, however, as the intrauterine environments of dizygotic-twin (separate placentas), monozygotic-twin (60-70% share one placenta), and singleton pregnancies (one placenta without competition for maternal nutrients) will all be diff erent, and this can be a confounder in the inter pretation of eff ects. 44A large study from Sweden on familial risk of type 2 diabetes showed that the relative risks were highest in individuals with at least two aff ected siblings, irrespective of parental diabetes status. 42This fi nding suggests that a recessive pattern of inheritance from uncommon genetic defects, the sharing of similar intrauterine, postnatal, or both environments by siblings (eg, breastfeeding or bottle feeding or childhood nutrition), or a combination of these factors is important.9][50] A greater number of these loci are associated with impaired -cell function (KCNJ11, TCF7L2, WFS1, HNF1B, SLC30A8, CDKAL1, IGF2BP2, CDKN2A, CDKN2B, NOTCH2, CAMK1D, THADA, KCNQ1, MTNR1B, GCKR, GCK, PROX1, SLC2A2, G6PC2, GLIS3, ADRA2A, and GIPR) than impaired insulin sensitivity (PPARG, IRS1, IGF1, FTO, and KLF14) or obesity (FTO). 38,48,50Of these, TCF7L2 is the strongest susceptibility locus for type 2 diabetes, being associated with -cell dysfunction. 48Most patients with monogenic forms of diabetes also have gene defects that aff ect islet -cell function. 51,52Nevertheless, only around 10% of the heritability of type 2 diabetes can be explained by susceptibility loci identifi ed so far, with each locus having a low eff ect size. 36The remaining heritability might be related to a large number of less common variants (allele frequency <5%) that are diffi cult to fi nd with current approaches of genome-wide association studies, and/or epigenetic phenomena.",
      "\t\n\nAnother component of T1D that aids in our understanding of the disease and assessment of risk is genetic inheritance.A longterm (up to 40 year) study of twin pairs in Finland revealed a monozygotic (MZ) pairwise concordance for T1D of 27.3% while the concordance for dizygotic (DZ) twins was 3.8% [4].The impact of genetics was further made clear in this study because upon diagnosis of T1D in one twin, the length of time to diagnosis in the other twin in the concordant pairs was a maximum of 6.9 years in MZ twins and 23.6 years in DZ twins [4].In addition to measuring incidence of T1D in twin studies, islet antigen-specific autoimmunity can also be determined.As a precursor to T1D, autoimmunity is defined as the presence of antibodies to islet autoantigens in sera [5].In another study, 83 unaffected monozygotic twins were followed for nearly 44 years and incidence of autoimmunity or diagnosis of T1D was recorded.This study showed a 65% cumulative incidence of T1D by 60 years of age and more than 75% tested positive for an islet autoantibody during the course of the study.Once autoimmunity was established, the risk of diabetes was 89% within 16 years of the first positive autoantibody test.\t\n\nClearly genetics play an important role in the T1D disease process as both MZ and DZ twins have the same environmental exposures but different concordance rates and length to diagnosis of the second twin.Numerous genes have been associated with T1D, the most significant being the HLA region on chromosome 6 [6].More than 90% of type 1 diabetics carry HLA alleles DR3-DQ2 or DR4-DQ8 compared to no more than 40% of the general population [7].Alleles at HLA-DQB1 are known to be, in part, protective [8].Single nucleotide polymorphisms (SNPs) are also associated with T1D.A recent genome-wide association study of approximately 2,000 patients with each of 7 common, chronic diseases, including T1D, and 7,000 shared controls confirmed the association of SNPs in 5 previously identified regions with T1D and discovered 5 novel associations.However, the authors concluded that these regions, with the exception of the HLA on chromosome 6, confer only modest effects on T1D, and ''the association signals so far identified account for only a small proportion of overall familiality'' [9].These results suggest that additional genetic variants contribute to inheritance of T1D.",
      "\t\n\nGenetic predisposition for the development of NIDDM has been strongly indicated by higher concor-dance rates in monozygotic than in dizygotic twins (Barnett et al., 1981;Newman et al., 1987), by clustering in families (Bennett, 1990), and by a strong correlation with the degree of population admixture (Zimmet et al., 1982;Chakraborty et al., 1986;Groop and Toumi, 1997).Although some rare monogenic forms of early onset NIDDM-like diseases in humans have been identified (reviewed in Froguel et al., 1997), genes responsible for the common forms of late-onset NIDDM remain unknown.Genome-wide scans for such genes have detected linkages of diabetes phenotypes with NIDDM1 on chromosome 2q in Mexican Americans (Hanis et al., 1996) and NIDDM2 on chromosome 12q in Finnish families (Mahtani et al., 1996).In a major effort, complex haplotypes in the Calpain 10 gene (CAPN10) at the NIDDM1 locus have recently been associated with increased risk for developing type II diabetes in Mexican Americans and Northern Europeans (Horikawa et al., 2000).CAPN10 is the first NIDDM gene cloned thus far.",
      "\t\n\nAlthough there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied.\t\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared",
      "\t\n\nThe marked increase of T1D incidence cannot be solely attributed to genetic risk (Snouffer, 2018).In fact, disease discordance in monozygotic twins (30-70%) strongly suggests environmental factors contribute to the aetiology of T1D (Redondo et al., 2008).These contributions may manifest through epigenetic modification including altered DNA methylation (Cepek et al., 2016;Paul et al., 2016;Stefan et al., 2014), which has been reported to play a key role in the transcriptional regulation of gene expression, and in some part, contributes to the aetiology of T1D (Stefan et al., 2014).Other environmental exposures attributable to the rising prevalence of T1D include diet (Hansen et al., 2006), gestational infections (Rei Lindehammer et al., 2012), and viral infections (Lnnrot et al., 2000).As such, it is highly likely that these non-genetic triggers interact with susceptibility genes in genetically predisposed individuals to influence the development of T1D.",
      "\t\n\nWhile these data indicate a major role for inborn susceptibility, they also underscore the role of environment and random chance.Secular trends in diet and physical activity are associated with a rising rate of T2D, demonstrating the impact of environment.Monozygotic twins are less than 100% concordant for both T1D and T2D, demonstrating that environment and/or random chance plays a major role in disease.Formal estimates of heritability (100) and long-term follow-up of monozygotic twins ascertained without disease bias (131) confirm the role of these nongenetic factors.",
      "\t\n\nGenetic susceptibility to type 1 diabetes (T1D) is well supported by epidemiologic evidence; however, disease risk cannot be entirely explained by established genetic variants identified so far.This study addresses the question of whether epigenetic modification of the inherited DNA sequence may contribute to T1D susceptibility.Using the Infinium HumanMethylation450 BeadChip array (450k), a total of seven long-term disease-discordant monozygotic (MZ) twin pairs and five pairs of HLA-identical, disease-discordant non-twin siblings (NTS) were examined for associations between DNA methylation (DNAm) and T1D.Strong evidence for global hypomethylation of CpG sites within promoter regions in MZ twins with TID compared to twins without T1D was observed.DNA methylation data were then grouped into three categories of CpG sites for further analysis, including those within: 1) the major histocompatibility complex (MHC) region, 2) non-MHC genes with reported T1D association through genome wide association studies (GWAS), and 3) the epigenome, or remainder of sites that did not include MHC and T1D associated genes.Initial results showed modest methylation differences between discordant MZ twins for the MHC region and T1D-associated CpG sites, BACH2, INS-IGF2, and CLEC16A (DNAm difference range: 2.2%e5.0%).In the epigenome CpG set, the greatest methylation differences were observed in MAGI2, FANCC, and PCDHB16, (DNAm difference range: 6.9%e16.1%).These findings were not observed in the HLA-identical NTS pairs.Targeted pyrosequencing of five candidate CpG loci identified using the 450k array in the original discordant MZ twins produced similar results using control DNA samples, indicating strong agreement between the two DNA methylation profiling platforms.However, findings for the top five candidate CpG loci were not replicated in six additional T1Ddiscordant MZ twin pairs.Our results indicate global DNA hypomethylation within gene promoter regions may contribute to T1D; however, findings do not support the involvement of large DNAm differences at single CpG sites alone in T1D.",
      "\tParticipants\n\nTwo cohorts of monozygotic (MZ) and dizygotic (DZ) twins discordant for type 1 diabetes were tested for TPOA to determine the relative influence of genetic and environmental factors.Initially, type 1 diabetes-discordant twin pairs were selected from the British Diabetic Twin Study [5] and a US twin cohort [4].The basic characteristics of the twins are shown in Table 1.These individuals fulfilled the following criteria: (1) twin pairs initially disease discordant; (2) both twins available for study; (3) neither twin receiving drugs other than human insulin; (4) all had normal plasma creatinine; and (5) diabetes initially excluded in the co-twin by OGTT and random whole-blood glucose <7.0 mmol/l.Monozygosity was established using both clinical data and DNA fingerprinting (data not shown) and type 1 diabetes was defined by standard criteria [9].\t\n\nChapter 3 evaluates the heritability of TPOA, which was estimated in type 1 diabetes discordant MZ and DZ twin pairs from UK and US twin cohorts.To address the problem of limited sample size and power, a meta-analysis was carried out using structural equation model fitting.We further investigated whether the same environmental factors that caused type 1 diabetes in discordant twin pairs also caused a higher risk of thyroid autoimmunity as defined by TPOA.",
      "\t\n\nTwin studies provide further evidence for heritability of type 1 diabetes susceptibility.Monozygotic twins are 100% genetically identical (excepting epigenetic events such as the rearrangements of immunoglobulin and T-cell receptor genes, which occur differently in each individual).Dizygotic twins share only 50% of their genetic material.In contrast to the difference in the degree of genetic similarity, both twin pairs are exposed to environmental factors that are likely equally similar for monozygotic and dizygotic twins (especially samesex dizygotic twins).Thus, the degree to which monozygotic twins show greater concordance for disease susceptibility compared with dizygotic twins indicates the degree to which genetic factors contribute to disease susceptibility [see Boomsma et al. (19) for review].\t\n\nPerhaps the most informative twin studies for this purpose are those based on large twin registries, because they avoid ascertainment bias that can confound clinic-based studies.In clinic-based studies, where ascertainment of a twin pair depends on at least one twin being affected, concordant affected pairs have two chances to be identified, whereas discordant pairs, with only one affected sibling, have only one chance.Thus, concordance rates can be overestimated using diagnosis-based ascertainment strategies [reviewed in Redondo et al. (20)].Prospective studies of initially discordant pairs can also be used to avoid this bias, and, furthermore, provide information about the rate of concordance over time.For type 1 diabetes, the concordance rate for monozygotic twins from these studies has been estimated as 21-53%, with most estimates between 30-50% [see Redondo et al. (20) and references therein].One study (21) estimated a cumulative concordance rate as high as 70%, adjusted for age of onset of the affected twin and last observation of the unaffected twin.As expected, the concordance rate increases over time as new diagnoses of diabetes are made (22).Interestingly, much of the risk to a co-twin is within the first 3 yr after the index twin's diagnosis (23), perhaps consistent with a shared environmental exposure, but also consistent with a genetic role in determining age of diagnosis (24).From the excess concordance in monozygotic compared with dizygotic twins, it has been estimated that as much as 66-72% of the variation in type 1 diabetes risk is attributable to genetic factors (21, 24a), although other studies have yielded lower estimates (23).This fraction is also referred to as the heritability, or h 2 .The concordance rate is much higher for monozygotic twins when one twin is diagnosed at an early age (23,25), suggesting that heritability might be highest for very early onset type 1 diabetes.\t\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.\tType 1 diabetes is an environmental disease\n\nThere are convincing data that non-genetic factors, perhaps environmental factors in early childhood, also play a role in diabetes susceptibility.First, the heritability of type 1 diabetes is only 72% or less, implying that at least a quarter of the risk of type 1 diabetes is not determined by inherited sequence variation.Consistent with this concept, the concordance rate for type 1 diabetes in monozygotic twins is estimated at 70% or less.It is possible that genetic events that distinguish monozygotic twins, such as the rearrangement of the T-cell receptor genes, or other stochastic, random events play a role in type 1 diabetes susceptibility.However, shared environmental factors are implicated by comparing the concordance rates for siblings and for dizygotic twins.While both siblings and dizygotic twins share 50% of their genetic material in common, dizygotic twins are exposed to a more similar environment than are siblings.Thus, the fact that dizygotic twins exhibit a higher rate of concordance for type 1 diabetes than do siblings [13 vs. 7% in Denmark; see Kyvik et al. (21)] implies that shared environmental factors affect the risk of type 1 diabetes.These shared environmental influences could be prenatal (intrauterine), or related to diet, infectious exposure, or other factors.Additional epidemiologic evidence confirms the importance of environmental factors: there is seasonality both in the month of birth (generally lower in winter) and in the month of diagnosis (generally peaking in winter), although the degree of seasonality varies among populations (12,(31)(32)(33)(34).",
      "\tMonogenic vs. polygenic diabetes\n\nMonogenic and polygenic diabetes are traditionally considered distinct, with monogenic diabetes resulting from one highly penetrant variant in one gene in a given individual, and polygenic diabetes resulting from the contribution of several variants with smaller effects in the context of environmental/lifestyle factors.In T1D, autoimmune dysfunction is the prominent mechanism, with variation in the major histocompatibility locus and other genomic factors combining with apparent environmental triggers to result in beta cell loss and diabetes.In monogenic diabetes, highly penetrant variants, mostly Finally, while lack of features of either autoimmunity or obesity/metabolic syndrome raises the likelihood of monogenic diabetes, these features can co-exist with monogenic diabetes, particularly obesity given its high prevalence especially in youth.In the Treatment Options for Diabetes in Adolescents and Youth (TODAY) clinical trial in which overweight or obesity was required for the newly diagnosed youth with T2D enrolled, at least 4.5% were identified as having MODY.Those with HNF4A-MODY had poor response to metformin, representing a previously missed opportunity for optimal treatment 9 .In summary, monogenic and polygenic forms of diabetes exist along more of a continuum than previously appreciated.Therefore, knowledge about P R E V I E W monogenic diabetes not only provides opportunities for etiology-based treatment of the minority of individuals with highly penetrant variants, but also informs broader understanding of diabetes etiology.",
      "\t\n\nRecent GWAS have successfully identified more than 40 independent T1DM-associated tagging SNPs; however, the sum of these loci does not fully explain the heritability estimated from familial studies [16].For example, twin studies have shown that for di-zygotic twins, the pairwise T1DM concordance rate is 10%, whereas for mono-zygotic twins, the concordance rate is approximately 50% [17].Thus, dietary and other environmental factors also influence T1DM incidence and development.These factors primarily include the use of breast milk vs. infant formula [18], highly hydrolyzed infant formula vs. conventional infant formula [19], early/late exposure to gluten [20] and vitamin D [21].Interestingly, a newly diagnosed child fed a gluten-free diet was shown to remain healthy without insulin therapy for 20 months [22].",
      "\t\n\nBecause close relatives of diabetic patients share common environmental factors, it could be argued that shared environment alone accounts for the increased risk of Type I diabetes among relatives of diabetic patients.More definitive evidence for a genetic basis is obtained by comparing the diabetes concordance rates in monozygotic (MZ, 100 % shared genes) and dizygotic twins (DZ, average 50 % shared genes), because twins experience similar environments both before and after birth.These studies have consistently shown a higher Type I diabetes concordance rate in MZ twins than DZ twins [1013], demonstrating a clear genetic basis for this disorder.The MZ twin concordance rate also provides a rough idea of the degree of genetic compared with non-genetic determination in specific environments.This concordance rate has been variously estimated as 34 % by age 30 [11], 43 % within 12 years of diagnosis of the index case [14], and 50 % within 40 years of index diagnosis [15], implying strong non-genetic factors (reflected as discordance) in the aetiology of Type I diabetes.",
      "\tType 1 Diabetes\n\nDiscordance rates in twins, the rise in global incidence, variance in geographic prevalence, and assimilation of local disease incidence rates when individuals migrate from low-to high-incidence countries all support an environmental influence on risk for developing type 1 diabetes.Furthermore, many lines of evidence suggest that environmental factors interact with genetic factors in both the triggering of autoimmunity and the subsequent progression to type 1 diabetes.Supporting this gene-environment interaction is the fact that most subjects with the highest-risk HLA haplotypes do not develop type 1 diabetes.",
      "\t\n\nIt is therefore intriguing that A1C levels are significantly correlated in monozygotic twins whether they are concordant for type 1 diabetes or not (4): in a discordant twin pair one twin is treated with insulin, whereas the other one isn't, and thus this degree of correlation suggests that genetic contributors to A1C may be detectable despite the superimposition of a strong environmental modifier.Rig-orous estimates of heritability of treated A1C, however, are not available."
    ],
    [
      "\tFuture directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4.",
      "\t\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential.",
      "\tGENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies.",
      "\t\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations.",
      "\tGenomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.",
      "\t\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484\t\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484\tPharmacogenetics in disease progression\n\nOver the recent years, more than 90 susceptibility genes have been identified by genome-wide association studies (GWAS) [55][56][57][58].However, the knowledge of the potential interactions between T2D predisposing genetic variants and the efficacy of treatment of T2D is sparse.Identification of gene-treatment interactions is challenging and requires large sample sizes and sophisticated analytical methods.Furthermore, detailed information on lifestyle and compliance to treatment as well as a long follow-up period are necessary for analysis of pharmacogenomics in T2D.\t\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D.",
      "\t\n\nGenetic determinants of diabetes and metabolic syndromes.",
      "\t\n\nOver the past two years, there has been a spectacular change in the capacity to identify common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes (T2D).The principal advance has been the ability to undertake surveys of genome-wide association in large study samples.Through these and related efforts, $20 common variants are now robustly implicated in T2D susceptibility.Current developments, for example in high-throughput resequencing, should help to provide a more comprehensive view of T2D susceptibility in the near future.Although additional investigation is needed to define the causal variants within these novel T2Dsusceptibility regions, to understand disease mechanisms and to effect clinical translation, these findings are already highlighting the predominant contribution of defects in pancreatic b-cell function to the development of T2D.",
      "\t\n\nThe availability of detailed information on gene  environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them.",
      "\t\n\nThere is strong evidence that novel T2D genes will be potentially exciting pharmaceutical targets.There is strong evidence in favour of this already, as the most established T2D susceptibility genes are also well-known drug targets, namely PPARG and thiazolidinediones [45] and KCNJ11 and sulfonylurea therapy [46,128].",
      "\tFUTURE PERSPECTIVES\n\nContinued investment in studies of G  E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G  E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G  E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.",
      "\t\n\nRegulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk.Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints.We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico followup in consortia.We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials.We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents.A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies.The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk.Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials.Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.\t\nRegulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk.Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints.We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico followup in consortia.We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials.We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents.A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies.The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk.Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials.Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.\tDISCUSSION\n\nAnticipating the side effects of drugs before phase 3 clinical trials could support drug discovery and development, reducing attrition rates and saving considerable time and money.The promise of human genetics in this endeavor (2, 3, 7, 27) depends on the availability of genetic variants that mimic pharmaceutical interventions.We undertook a systematic study to identify such genetic variants in the context of diabetes and obesity and identified an association between fasting glucose and T2D with a missense variant in GLP1R, the gene encoding the GLP-1 receptorthe target of the GLP1R agonist class of T2D therapies.Regulatory authorities require evidence that therapies for T2D are not associated with unacceptable increases in cardiovascular risk.The reduced risk associated with the glucose-lowering genetic variant in GLP1R provides evidence that not only will GLP1R agonists meet this regulatory hurdle but they may also reduce CHD events.Ongoing trials of GLP1R agonists are designed to resolve this uncertainty and will also augment the evidence on the broader validity of genetic approaches in drug target validation.",
      "\tConclusions\n\nRecent large collaborative studies to clarify the genetics of T2DM have identified variants in nine gene areas that are associated with a moderately increased risk of developing the disease.Further studies may identify more of these variants and ultimately improve the possibility of predicting disease risk in healthy subjects.Search for the patho-physiological role of these variants has not been easy, although evidence is emerging for their involvement in either pancreatic development or in the control of insulin secretion.The elucidation of novel pathways involved to the etiology of T2DM may contribute to improved prevention and treatment of the disease.The influence of environmental factors such as lifestyle and diet must not be overlooked, and future studies should be especially focused on the interactions between dietary factors and the genetic variants involved in T2DM risk.In the light of the recent investigative efforts, the genetics of T2DM is probably no longer ''the geneticist's nightmare'', but it certainly remains an intriguing puzzle that is yet to be solved.",
      "\t\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis.\t\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis."
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