{
  "question": [
    "How do genes influence how our bodies respond to sugar and insulin?",
    "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?",
    "How can new gene therapies potentially cure or treat diabetes?",
    "What is the difference between monogenic and polygenic diabetes?",
    "How does studying the DNA of people with diabetes help scientists find better treatments or cures?"
  ],
  "answer": [
    "Genes play a crucial role in how our bodies respond to sugar and insulin. Certain gene variants can impair insulin sensitivity and affect glucose, fatty acid, and energy metabolism at both cellular and whole-body levels. For instance, abnormalities in the genes encoding for proteins involved in the insulin-signaling network can cause insulin resistance and related traits. This can lead to metabolic diseases like type 2 diabetes, obesity, and cardiovascular diseases. Additionally, genetic mutations can severely alter insulin sensitivity. Some genes also play a role in insulin secretion by pancreatic -cells, which is crucial for maintaining normal glucose levels.",
    "Yes, there are specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes. For Type 1 diabetes, the greatest genetic risk is conferred by specific alleles, genotypes, and haplotypes of the HLA class II (and class I) genes. There are also about 50 non-HLA region loci that affect the Type 1 diabetes risk. For Type 2 diabetes, the risk is higher if one parent has the disease, particularly if the mother has it. The disease is polygenic, with many common variants contributing to overall disease risk.",
    "New gene therapies can potentially treat diabetes by targeting specific genetic variations that affect the response to certain drugs. For example, genetic variation in the organic cation transporter 1 (OCT1) has been found to affect the response to the diabetes drug metformin. Understanding these gene-drug interactions can lead to more personalized and effective treatment strategies. Additionally, therapies that slow the loss of -cell function, which is a characteristic of type 2 diabetes, could provide more durable glucose control. Incretin-based therapies, which improve -cell health, could potentially slow disease progression. Furthermore, the use of nanotechnology in gene therapies could introduce novel strategies for glucose measurement and insulin delivery.",
    "Monogenic diabetes is a form of the disease that results from mutations in a single gene. It is characterized by high phenotypic penetrance, meaning the presence of the mutation almost certainly leads to the development of the disease. On the other hand, polygenic diabetes is a form of the disease that results from the combined effect of mutations in multiple genes. Each of these mutations contributes a small amount of risk, and the disease typically also requires a permissive environment to develop.",
    "Studying the DNA of people with diabetes helps scientists identify key biological processes and genes involved in the disease's pathogenesis. This can lead to the discovery of novel drug targets for the disease. Additionally, understanding genetic variants can influence an individual's response to therapy, paving the way for personalized medicine. Furthermore, advancements in genomics and genetic testing can help identify individuals at risk of developing diabetes, enabling early intervention and prevention strategies."
  ],
  "contexts": [
    [
      "\t\n\nElucidating the potential mechanisms involved in the detrimental effect of excess body weight on insulin action is an important priority in counteracting obesityassociated diseases.The present study aimed to disentangle the epigenetic basis of insulin resistance by performing a genome-wide epigenetic analysis in visceral adipose tissue (VAT) from morbidly obese patients depending on the insulin sensitivity evaluated by the clamp technique.The global human methylome screening performed in VAT from 7 insulin-resistant (IR) and 5 insulin-sensitive (IS) morbidly obese patients (discovery cohort) analyzed using the Infinium HumanMethyla-tion450 BeadChip array identified 982 CpG sites able to perfectly separate the IR and IS samples.The identified sites represented 538 unique genes, 10% of which were diabetes-associated genes.The current work identified novel IR-related genes epigenetically regulated in VAT, such as COL9A1, COL11A2, CD44, MUC4, ADAM2, IGF2BP1, GATA4, TET1, ZNF714, ADCY9, TBX5, and HDACM.The gene with the largest methylation fold-change and mapped by 5 differentially methylated CpG sites located in island/shore and promoter region was ZNF714.This gene presented lower methylation levels in IR than in IS patients in association with increased transcription levels, as further reflected in a validation cohort (n 5 24; 11 IR and 13 IS).This study reveals, for the first time, a potential epigenetic regulation involved in the dysregulation of VAT that could predispose patients to insulin resistance and future type 2 dia-1 Both authors equally contributed to this work.\t\nElucidating the potential mechanisms involved in the detrimental effect of excess body weight on insulin action is an important priority in counteracting obesityassociated diseases.The present study aimed to disentangle the epigenetic basis of insulin resistance by performing a genome-wide epigenetic analysis in visceral adipose tissue (VAT) from morbidly obese patients depending on the insulin sensitivity evaluated by the clamp technique.The global human methylome screening performed in VAT from 7 insulin-resistant (IR) and 5 insulin-sensitive (IS) morbidly obese patients (discovery cohort) analyzed using the Infinium HumanMethyla-tion450 BeadChip array identified 982 CpG sites able to perfectly separate the IR and IS samples.The identified sites represented 538 unique genes, 10% of which were diabetes-associated genes.The current work identified novel IR-related genes epigenetically regulated in VAT, such as COL9A1, COL11A2, CD44, MUC4, ADAM2, IGF2BP1, GATA4, TET1, ZNF714, ADCY9, TBX5, and HDACM.The gene with the largest methylation fold-change and mapped by 5 differentially methylated CpG sites located in island/shore and promoter region was ZNF714.This gene presented lower methylation levels in IR than in IS patients in association with increased transcription levels, as further reflected in a validation cohort (n 5 24; 11 IR and 13 IS).This study reveals, for the first time, a potential epigenetic regulation involved in the dysregulation of VAT that could predispose patients to insulin resistance and future type 2 dia-1 Both authors equally contributed to this work.",
      "\tElucidate the pathogenesis linking obesity and type 2 diabetes\n\nA better understanding of mechanisms linking obesity, insulin resistance, and type 2 diabetes may ultimately facilitate more individualized treatment.One future research priority is to clarifty how identified gene variants affect glucose, fatty acid, and energy metabolism at both cellular and whole-body levels.Rather than searching for a single factor or theory explaining the predisposition to -cell decompensation in obese individuals, a multifactorial, synergistic explanation seems more compatible with current knowledge.Multiple mechanisms may link -cell dysfunction to systemic insulin resistance, including differing cellular responses to nutrient excess and impaired brain neurocircuits governing energy homeostasis.One way to approach this complex pathophysiology is to examine glucose-tolerant obese patients and study the association with and progression to -cell decompensation.",
      "\t\n\nWe began the investigation by focusing on insulin-signaling genes, a natural and well-established candidate for finding a signature set of genes associated with insulin resistance or diabetes [9].In particular, by examining microarray data, we attempted to detect a statistically significant, transcriptional alteration in a set of insulin-signaling genes in diabetic tissue compared to normal.Surprisingly, using existing analytical methods, we were unable to detect such alterations in microarray data produced in several human studies.Using sophisticated and remarkably sensitive techniques, previous studies identified the oxidative phosphorylation pathway as transcriptionally down-regulated in diabetic muscle tissue compared to normal [10,11].However, insulin-signaling gene sets were not detected to be transcriptionally altered, using state of the art analyses, more than expected by chance.\tAuthor Summary\n\nType 2 diabetes mellitus currently affects millions of people.It is clinically characterized by insulin resistance in addition to an impaired glucose response and associated with numerous complications including heart disease, stroke, neuropathy, and kidney failure, among others.Accurate identification of the underlying molecular mechanisms of the disease or its complications is an important research problem that could lead to novel diagnostics and therapy.The main challenge stems from the fact that insulin resistance is a complex disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways.In this report, the authors develop a network-based methodology that appears to be more sensitive than previous approaches in detecting deregulated molecular processes in a disease state.The methodology revealed that both insulin signaling and nuclear receptor networks are consistently and differentially expressed in many models of insulin resistance.The positive results suggest such network-based diagnostic technologies hold promise as potentially useful clinical and research tools in the future.affected in the disease state. (3) Evaluate the hypothesis that genes in a given gene set are observed in a higher proportion (i.e., enriched) than expected by chance in the HSN and repeat for each gene set in the assembly.Repeat (2) and (3) for every insulin resistant or diabetic condition compared to normal in the dataset. (4) Order the gene sets of interest based on the number of different HSNs where they appear enriched. (5) For each gene set, assign a p-value to the number of conditions where it is enriched.The gene sets with a significant p-value are taken as transcriptionally affected across a broad set of diabetes-related models.Consistent with the stated goal of GNEA, gene sets enriched in a few conditions, while potentially interesting in their own right, will not generally be assigned a significant p-value (Figure 1).",
      "\tIn addition, we have\ndetermined the effects of these modifications on the pattern of gene expression\nin each tissue, and how insulin signaling might interact with nuclear receptor\nsignaling in insulin resistance. Tissues of particular importance in development\nof type 2 diabetes and the metabolic syndrome include the liver, brain and fat. In liver, for example, insulin action through IRS-1 and Akt is involved in control\nof glucose production, while insulin action through IRS-2 and atypical PKCs is\nmore involved in hepatic lipogenesis.",
      "\tExercise training and the Ala allele must act either independently or in synergy\nto modify glucose homeostasis through increasing glucose uptake or by decreasing\nhepatic glucose output. At the whole body level, exercise training has been shown\nto increase insulin sensitivity (Borghouts & Keizer 2000, Short et al 2003, Duncan\net al 2003) and has also been shown to decrease basal hepatic glucose production\nin patients with type 2 diabetes (Segal et al 1991).",
      "\tIV. Gene Variants Affecting Insulin Sensitivity\n\nInsulin resistance provokes a critical challenge for the pancreatic -cell that has to be compensated for by increments in insulin secretion to maintain normoglycemia.Thus, genetically determined -cell defects may only become apparent in the presence of insulin resistance (9,247).Insulin resistance is therefore considered an early and crucial step in the pathogenesis of type 2 diabetes.Undoubtedly, insulin resistance is strongly associated with obesity.Although the cause-effect relationship is far from being clear, insulin resistance is often suggested to result from obesity and to be predominantly caused by environmental factors, such as high-caloric diet and/or physical inactivity (248,249).However, the genetic investigations of the last 10 yr revealed that certain gene variants impair insulin sensitivity without influencing the overall fat mass.Recent advances in the field, mainly based on candidate gene approaches, also strengthen the role of genetics in the establishment of insulin resistance.",
      "\t\n\nKey components of the insulin signaling pathways have also been tested.They were at fi rst thought to be important players in the context of the insulin resistance of T2DM.Several of these genes are also expressed in pancreatic  -cells, and several studies from knockout animals have demonstrated that they may also have an important role in the mechanisms of insulin secretion [23,24] .More than 50 different mutations have been found in the coding regions of the insulin receptor gene on chromosome 19p (see Chapter 15 ) [67] ; patients with these mutations seldom present with the common form of T2DM [68] , but rather with a syndrome of severe insulin resistance associated with leprechaunism, or with acanthosis nigricans, hirsutism and major hyperinsulinemia [69] .Missense variants in the gene encoding the fi rst substrate for the insulin receptor kinase ( IRS1 ) on chromosome 2q have been detected in several populations [70 -73] but an association of these variants with diabetes was not observed in all studies [74,75] .",
      "\t\n\nFigure 2: Role of genes and the environment in development of obesity and type 2 diabetes Interaction of genes that aff ect body adiposity with environmental factors results in development of obesity and associated insulin resistance.However, only when genes for abnormal -cell function are present along with those for body adiposity does interaction with the environment result in development of type 2 diabetes.\t\n\nGlucose metabolism is normally regulated by a feedback loop including islet  cells and insulin-sensitive tissues, in which tissue sensitivity to insulin aff ects magnitude of -cell response.If insulin resistance is present,  cells maintain normal glucose tolerance by increasing insulin output.Only when  cells cannot release suffi cient insulin in the presence of insulin resistance do glucose concentrations rise.Although -cell dysfunction has a clear genetic component, environmental changes play an essential part.Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and -cell dysfunction, and the potential role of changes in the microbiome.Several new approaches for treatment have been developed, but more eff ective therapies to slow progressive loss of -cell function are needed.Recent fi ndings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse eff ects of these interventions.However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful eff ects of this disease.",
      "\t\n\nGenetic studies of IL6 and IL6R in type 2 diabetes and insulin resistance",
      "\t\n\nInsulin resistance has a central role in the pathogenesis of several metabolic diseases, including type 2 diabetes, obesity, glucose intolerance, metabolic syndrome, atherosclerosis, and cardiovascular diseases.Insulin resistance and related traits are likely to be caused by abnormalities in the genes encoding for proteins involved in the composite network of insulin-signaling; in this review we have focused our attention on genetic variants of insulin-signaling inhibitor molecules.These proteins interfere with different steps in insulin-signaling: ENPP1/PC-1 and the phosphatases PTP1B and PTPRF/LAR inhibit the insulin receptor activation; INPPL1/SHIP-2 hydrolyzes PI3-kinase products, hampering the phosphoinositide-mediated downstream signaling; and TRIB3 binds the serine-threonine kinase Akt, reducing its phosphorylation levels.While several variants have been described over the years for all these genes, solid evidence of an association with type 2 diabetes and related diseases seems to exist only for rs1044498 of the ENPP1 gene and for rs2295490 of the TRIB3 gene.However, overall the data recapitulated in this Review article may supply useful elements to interpret the results of novel, more technically advanced genetic studies; indeed it is becoming increasingly evident that genetic information on metabolic diseases should be interpreted taking into account the complex biological pathways underlying their pathogenesis.\t\nInsulin resistance has a central role in the pathogenesis of several metabolic diseases, including type 2 diabetes, obesity, glucose intolerance, metabolic syndrome, atherosclerosis, and cardiovascular diseases.Insulin resistance and related traits are likely to be caused by abnormalities in the genes encoding for proteins involved in the composite network of insulin-signaling; in this review we have focused our attention on genetic variants of insulin-signaling inhibitor molecules.These proteins interfere with different steps in insulin-signaling: ENPP1/PC-1 and the phosphatases PTP1B and PTPRF/LAR inhibit the insulin receptor activation; INPPL1/SHIP-2 hydrolyzes PI3-kinase products, hampering the phosphoinositide-mediated downstream signaling; and TRIB3 binds the serine-threonine kinase Akt, reducing its phosphorylation levels.While several variants have been described over the years for all these genes, solid evidence of an association with type 2 diabetes and related diseases seems to exist only for rs1044498 of the ENPP1 gene and for rs2295490 of the TRIB3 gene.However, overall the data recapitulated in this Review article may supply useful elements to interpret the results of novel, more technically advanced genetic studies; indeed it is becoming increasingly evident that genetic information on metabolic diseases should be interpreted taking into account the complex biological pathways underlying their pathogenesis.",
      "\tConclusion\n\nWe would propose that it is highly probable that more insulin resistance than b-cell dysfunction T2DM susceptibility genes remain undiscovered at the present time, most likely due to problems associated with study design and the complex nature of physiological responses to nutrients and insulin.In addition, it must be understood that even with 38 genes identified relevant to T2DM pathophysiology, the risk conferred by these combined genes accounts for only a small proportion of overall risk.It must be remembered that the rapid changes in T2DM incidence and prevalence observed in recent decades are a result of the interaction of a stable genetic background with a rapidlychanging environment.Future intervention at newly-discovered insulin secretion controlling loci should improve b-cell function allowing a more robust defence against environmental insult.Targeting oxidative stress, metabolic stress and low grade inflammation may provide fruitful avenues.However, novel therapeutic approaches, whether pharmacological or nonpharmacological, which can target the effects of diet-induced obesity on tissue-specific insulin resistance in the early pathogenesis of T2DM remain a central and invaluable goal of research aiming to halt the rapidly-increasing prevalence of T2DM and its complications worldwide.\tWhy the paucity of genes involved in insulin resistance\n\nrelative to b-cell function?\t\n\nHowever, these cases provide evidence for the existence of genetic mutations that can severely alter insulin sensitivity.It remains possible therefore that the paucity of insulin resistance genes found by GWAS may be at least in part explained by the relative difficulty of accurately measuring small variations in insulin sensitivity compared to measuring small changes in insulin secretion in large populations.",
      "\t\n\nBaboons also show patterns similar to humans with respect to insulin resistance.Insulin resistance-related phenotypes were significantly heritable in baboons (Cai et al. 2004;Tejero, Freeland-Graves et al. 2004).We showed that one set of genes contributing to insulin resistance also appeared to influence adiposity-related phenotypes, which revealed a common genetic basis for development of insulin resistance and obesity (Cai et al. 2004).Variation in glucose transporter 4 (GLUT4) mRNAwas found to be under significant genetic influence and was genetically correlated with plasma insulin and body weight, supporting their regulation by a common set of genes (Tejero, Proffitt et al. 2004).",
      "\t\n\nI nsulin resistance precedes and predicts the development of type 2 diabetes mellitus (DM) (1,2).Defects in insulin signal transduction, gene expression, and muscle glycogen synthesis, and accumulation of intramyocellular triglycerides have all been identified as potential mediators of insulin resistance in high-risk individuals (1,(3)(4)(5)(6)(7).However, the molecular pathogenesis of DM remains unknown.Mouse data highlight the importance of glucose uptake into muscle but suggest a role for novel mechanisms, distinct from insulin signaling pathways (8).The importance of genetic risk factors is exemplified by the high concordance of DM in identical twins, the strong influence of family history and ethnicity on risk, and the identification of DNA sequence alterations in both rare and common forms of DM (9).Environmental factors, including obesity, inactivity, and aging, also play critical roles in DM risk.Because both genotype and environment converge to influence cellular function via gene and protein expression, we hypothesize that alterations in expression define a phenotype that parallels the metabolic evolution of DM and provides potential clues to pathogenesis.We used high-density oligonucleotide arrays to identify genes differentially expressed in skeletal muscle from nondiabetic and type 2 diabetic subjects.Because hyperglycemia per se can modulate expression, we also evaluated gene expression in insulin-resistant subjects at high risk for DM (''prediabetes'') on the basis of family history of DM and Mexican-American ethnicity (10).We demonstrate that prediabetic and diabetic muscle is characterized by decreased expression of oxidative phosphorylation genes, many of which are regulated by nuclear respiratory factor (NRF)-dependent transcription.Further-more, expression of peroxisomal proliferator activator receptor  coactivator (PGC1) and - (PPARGC1 and PERC), coactivators of both PPARG and NRF-dependent transcription, is significantly reduced in both prediabetic and diabetic subjects.Taken together, these data indicate that decreased PGC1 expression may be responsible for decreased expression of NRFdependent metabolic and mitochondrial genes and may contribute to the metabolic disturbances characteristic of insulin resistance and DM.",
      "\t\n\nStudies carried out to identify genetic and nongenetic components participating in homeostatic regulation of glucose and in T2D physiopathology have identified insulin resistance as a postreceptor defect that ultimately affects translocation of the glucose transporter GLUT4 toward the cell surface [9,10].The transduction of insulin signals is mediated by a series of phosphorylation cascades linked to the initial activation of the tyrosine kinase receptor of insulin and its action on the substrates of the insulin receptors (insulin receptor substrate IRS1, IRS-2, IRS-3, and IRS-4) [11].Tyrosine phosphorylation of IRS1 and its binding to phosphatidylinositol 3-kinase are critical events in the insulin signaling cascade leading to insulin-stimulated glucose transport. [12].The importance of IRS1 in insulin signaling has been confirmed in studies showing that this gene plays a very important role not only in peripheral insulin sensitivity, but also in the regulation of insulin secretion by pancreatic -cells [12,13].In addition, IRS1 knockout mice adipocytes showed considerable decrease in glucose transport and in the translocation of GLUT4 to the plasma membrane as a response to insulin [14].Insulin receptor substrate-1, whose gene is located in chromosome 2q36, has 21 sites for tyrosine kinase phosphorylation, which are responsible for most of its enzymatic function."
    ],
    [
      "\tA. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is 10 -15 times greater.Although 70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3).",
      "\t\n\nPrevious reports show that in the Japanese population, the frequency of a positive family history of diabetes in Japanese diabetic patients is particularly higher in parents of young-onset type 2 diabetic patients and lower in patients who have maximal BMI 35 kg/m 2 compared with those with maximal BMI 30 kg/m 2 (17).Therefore, we considered BMI and age at onset as possible covariates and/or confounding factors.We designed two subset populations, the first one is the subset termed Young-Onset45, in which both siblings were 45 years of age at diagnosis, and which includes the younger-diagnosed 20% families.The other is the subset termed Lean30, in which both siblings have a maximal BMI 30 kg/m 2 (Table 3).Genotyping of microsatellite markers.Genomic DNA was isolated from whole blood using the phenol-chloroform method.Genotyping was performed using a fluorescence-labeled human linkage mapping set (PE-LMSV2) comprising 400 highly informative microsatellite markers with an average intermarker spacing of 9.7 cM.Multiplex PCR conditions were set up for each of the 28 panels to amplify the 400 markers in 87 PCRs.PCR (95C for 12 min, then 40 cycles at 94C for 15 min, 55C for 15 min, 72C for 30 min, and 72C for 10 min) was performed with a 384-well plate on a GeneAmp PCR system (9700 Biblock; Perkin-Elmer, Foster City, CA) using the following (in 10-l reactions): 20 -40 ng genomic DNA, 2.5 mmol/l MgCl 2 , 0.25 mmol/l dNTPs (Pharmacia), variable amounts (0.2-1.5 pmol) of 5 and 3 primers, and 0.4 units AmpliTaq Gold DNA polymerase (Perkin-Elmer) in 1 PCR buffer II (Perkin-Elmer). (Multiplex PCR conditions are available from the authors on request. )An automated 96-channel pipettor Multimek 96 (Beckman) was used for the pipetting steps.Pooled amplification products were electrophoresed through 5% polyacrylamide gels (Long Ranger Singel Pack; Perkin Elmer) for 1.5 h at 2,000 V on 24-cm plates on an ABI 377 DNA sequencer.Semiautomated fragment sizing was performed by using Genescan 3.0 software (ABI), followed by allele calling with Genotyper 2.1 software (ABI).Some panels were electrophoresed on a multicapillary ABI 3700 sequencer and analyzed by Genescan-2.1 software (Perkin-Elmer).Among 400 markers in PE-LMSV2, eight markers (D1S214, D1S252, D3S2338, D3S1285, D4S1534, D7S640, D15S153, and D19S221) were not included because of technical problems.",
      "\t\nType 1 diabetes (T1D) tends to cluster in families, suggesting there may be a genetic component predisposing to disease.However, a recent large-scale genome-wide association study concluded that identified genetic factors, single nucleotide polymorphisms, do not account for overall familiality.Another class of genetic variation is the amplification or deletion of .1 kilobase segments of the genome, also termed copy number variations (CNVs).We performed genome-wide CNV analysis on a cohort of 20 unrelated adults with T1D and a control (Ctrl) cohort of 20 subjects using the Affymetrix SNP Array 6.0 in combination with the Birdsuite copy number calling software.We identified 39 CNVs as enriched or depleted in T1D versus Ctrl.Additionally, we performed CNV analysis in a group of 10 monozygotic twin pairs discordant for T1D.Eleven of these 39 CNVs were also respectively enriched or depleted in the Twin cohort, suggesting that these variants may be involved in the development of islet autoimmunity, as the presently unaffected twin is at high risk for developing islet autoimmunity and T1D in his or her lifetime.These CNVs include a deletion on chromosome 6p21, near an HLA-DQ allele.CNVs were found that were both enriched or depleted in patients with or at high risk for developing T1D.These regions may represent genetic variants contributing to development of islet autoimmunity in T1D.",
      "\t\n\nmore frequently than by chance alone among siblings who share the phenotype of type 1 diabetes.Nuclear families, or even just the affected sibling pairs themselves, are genotyped with panels of markers spanning the genome at a modest density.Linkage between a marker and a susceptibility locus for type 1 diabetes is determined by accumulating evidence across families.Since affected sibling pairs are relatively rare in type 1 diabetes, data from linkage studies are collected from a rather unique subgroup of families with type 1 diabetes.In general, linkage studies are the method of choice when the risk factors being sought have large effect sizes but are relatively rare.As risk factors become more common and have smaller effect sizes, association methods emerge as a potentially more powerful approach (Fig. 1).Since the genetic basis of type 1 diabetes is probably a complex mixture of small, moderate, and large genetic effects, multiple strategies are needed and vary according to the population being studied and their exposure to unknown environmental factors.\tDise a se Pr edic tion\n\nCurrent approaches for the prediction of type 1 diabetes take advantage of the major genetic risk factors, genotyping for HLA-DR and HLA-DQ loci (which is then combined with family history), and screening for autoantibodies directed against islet-cell antigens. 43,44The individual distribution of specific risk alleles correlates with gradations in disease penetrance, enabling a tiered staging strategy for the prediction of type 1 diabetes.For example, children who carry both of the highestrisk HLA haplotypes (DR3-DQ2 and DR4-DQ8) have a risk of approximately 1 in 20 for a diagnosis of type 1 diabetes by the age of 15 years. 45If the child has a sibling who has diabetes and the same haplotypes, the risk is even higher (approximately 55%). 46Since this haplotype combination occurs in only 2.3% of the white population, it is possible to envision universal screening strategies that pinpoint this highest-risk group.Inclusion of additional moderate HLA risk haplotypes and screening for autoantibodies would add cost and complexity to a population-screening approach but have the potential to identify the majority of all children with diabetes before the onset of the disease.If this were possible, then tests of potential preventive strategies could be performed, as outlined later in this article.The large number of new risk loci for type 1 diabetes that were recently identified from genomewide association studies could be added to these prediction schemes.These genetic factors are relatively easy, inexpensive, and noninvasive to measure and can be detected well before other features, such as autoantibodies, would typically develop.\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\nMore than 60 susceptibility loci have been identified (Table 1).The greatest genetic risk (50%) for T1D is conferred by alterations to immune genes, especially those encoding the classical HLAs (Ounissi-Benkalha and Polychronakos, 2008).Other genetic loci (Table 1) are believed to influence population-level risk for T1D, although it is poorly understood how these non-HLA loci contribute to disease susceptibility (Ram et al., 2016a).\tThe genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008).",
      "\tType 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease.\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.",
      "\tCONCLUSION\n\nThe greatest genetic risk (both increased risk, susceptible, and decreased risk, protective) for type 1 diabetes is conferred by specific alleles, genotypes, and haplotypes of the HLA class II (and class I) genes.There are currently about 50 non-HLA region loci that also affect the type 1 diabetes risk.Many of the assumed functions of the non-HLA genes of interest suggest that variants at these loci act in concert on the adaptive and innate immune systems to initiate, magnify, and perpetuate -cell destruction.The clues that genetic studies provide will eventually help lead us to identify how -cell destruction is influenced by environmental factors.While there is extensive overlap between type 1 diabetes and other immune-mediated diseases, it appears that type 1 and type 2 diabetes are genetically distinct entities.These observations may suggest ways to help identify causal gene(s) and, ultimately, a set of disease-associated variants defined on specific haplotypes.Unlike other complex human diseases, relatively little familial clustering remains to be explained for type 1 diabetes.The remaining missing heritability for type 1 diabetes is likely to be explained by as yet unmapped common variants, rare variants, structural polymorphisms, and gene-gene and/or gene-environmental interactions, in which we can expect epigenetic effects to play a role.The examination of the type 1 diabetes genes and their pathways may reveal the earliest pathogenic mechanisms that result in the engagement of the innate and adaptive immune systems to produce massive -cell destruction and clinical disease.The resources established by the international T1DGC are available to the research community and provide a basis for future discovery of genes that regulate the earliest events in type 1 diabetes etiology-potential targets for intervention or biomarkers for monitoring the effects and outcomes of potential therapeutic agents.",
      "\t\n\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes.\t\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes.\t\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al.\tIntroduction\n\nOver 60 loci in the genome contribute to genetic predisposition to type 1 diabetes (T1D) [1][2][3][4][5] in which insulin deficiency results from an autoimmune attack against insulin-producing beta cells of the pancreatic islets.Heterogeneity in the disease aetiology is recently acknowledged and immunological processes leading to T1D in individuals diagnosed later in life appear different from the processes in individuals having disease onset in early childhood, in which B cells are involved in the pathological process in the pancreas [5].Different genes and genetic variants may thus affect disease course at varying ages, also suggested by the high diagnosis age correlation (r 2 = 0.95) in Finnish monozygotic twins concordant for T1D [6].Of the known T1D risk loci, however, only the HLA locus and a few non-HLA loci, have been associated with age at diagnosis [7][8][9][10].Genetic risk score combines risk-increasing alleles into a single score and the genetic risk score for T1D has already been suggested for clinical use for screening of infants at highest T1D risk [11].All disease-susceptibility variants are included in the score, but only a few known T1D variants have stronger effects in individuals with early-onset disease [10].",
      "\tGenetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin.\tType 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.",
      "\t\n\nType 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40].",
      "\t\n\nType 1 diabetes risk stratification by T1D family history and HLA genotyping",
      "\t\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66"
    ],
    [
      "\t\n\nType 2 diabetes mellitus affects 9.6% of the adults in the United States and more than 200 million people worldwide.Diabetes can be a devastating disease, but it can now be treated with nine classes of approved drugs (insulins, sulfonylureas, glinides, biguanides, -glucosidase inhibitors, thiazolidinediones, glucagon-like peptide 1 mimetics, amylin mimetics, and dipeptidyl peptidase 4 inhibitors), in addition to diet and exercise regimens.Choosing which drug to give a patient is based on efficacy and also availability, cost, safety, tolerability, and convenience.Personalized medicine promises a path for individually optimized treatment choices, but realizing this promise will require a more comprehensive characterization of disease and drug response.In this issue of the JCI, Shu et al. make significant progress by integrating diverse data supporting the hypothesis that genetic variation in organic cation transporter 1 (OCT1) affects the response to the widely used biguanide metformin (see the related article beginning on page 1422).We discuss metformin, OCT1, pharmacogenetics, and how the integrative genomics revolution is likely to change our understanding and treatment of diabetes.\t\n\nType 2 diabetes mellitus affects 9.6% of the adults in the United States and more than 200 million people worldwide.Diabetes can be a devastating disease, but it can now be treated with nine classes of approved drugs (insulins, sulfonylureas, glinides, biguanides, -glucosidase inhibitors, thiazolidinediones, glucagon-like peptide 1 mimetics, amylin mimetics, and dipeptidyl peptidase 4 inhibitors), in addition to diet and exercise regimens.Choosing which drug to give a patient is based on efficacy and also availability, cost, safety, tolerability, and convenience.Personalized medicine promises a path for individually optimized treatment choices, but realizing this promise will require a more comprehensive characterization of disease and drug response.In this issue of the JCI, Shu et al. make significant progress by integrating diverse data supporting the hypothesis that genetic variation in organic cation transporter 1 (OCT1) affects the response to the widely used biguanide metformin (see the related article beginning on page 1422).We discuss metformin, OCT1, pharmacogenetics, and how the integrative genomics revolution is likely to change our understanding and treatment of diabetes.\t\nType 2 diabetes mellitus affects 9.6% of the adults in the United States and more than 200 million people worldwide.Diabetes can be a devastating disease, but it can now be treated with nine classes of approved drugs (insulins, sulfonylureas, glinides, biguanides, -glucosidase inhibitors, thiazolidinediones, glucagon-like peptide 1 mimetics, amylin mimetics, and dipeptidyl peptidase 4 inhibitors), in addition to diet and exercise regimens.Choosing which drug to give a patient is based on efficacy and also availability, cost, safety, tolerability, and convenience.Personalized medicine promises a path for individually optimized treatment choices, but realizing this promise will require a more comprehensive characterization of disease and drug response.In this issue of the JCI, Shu et al. make significant progress by integrating diverse data supporting the hypothesis that genetic variation in organic cation transporter 1 (OCT1) affects the response to the widely used biguanide metformin (see the related article beginning on page 1422).We discuss metformin, OCT1, pharmacogenetics, and how the integrative genomics revolution is likely to change our understanding and treatment of diabetes.",
      "\tA small number of medications\nthat are currently approved for the treatment of T2DM, including metformin, GLP1 receptor\nagonists and SGLT2 inhibitors, have been or are being evaluated as adjuncts to insulin\ntherapy in patients with T1DM275. For instance, the addition of metformin to insulin therapy\ndid not significantly improve glycaemic control in children276 or adults with T1DM277 but\nprovided a modest reduction in total daily insulin dose and body mass index.",
      "\t\n\nThe best example of pharmacogenetics has been in the treatment of patients with PNDM resulting from mutations in the Kir6.2 and SUR1 subunits of the K ATP channel.These patients frequently present with ketoacidosis and no detectable endogenous insulin secretion, and therefore insulin injections are the only treatment option.Insulin treatment is difficult in a young child, and outstanding glycemic control is rarely achieved.Finding that one-third of the patients with PNDM had mutations in the Kir6.2 channel that reduced channel closure in response to ATP led to the possibility of treating these patients with sulfonylureas that close the channel by an ATP-independent route (4,42).It was then possible to replace insulin injections with high-dose oral sulfonylureas in 90% of patients and also to achieve improved glycemic control without an increase in hypoglycemia (43,44).Insulin secretion is regulated despite the -cell having a limited response to ATP; this is predominantly mediated through nonclassical pathways for insulin secretion, particularly GLP1 (43).Excellent glycemic control is also seen in the majority of patients with SUR1 mutations treated with sulfonylureas (45).Therefore, 50% of patients diagnosed before 6 months with permanent diabetes can benefit greatly from a molecular diagnosis.To date, patients with K ATP channel mutations have maintained near normoglycemia for over 4 years (A.T.H., unpublished data).Doses tend to reduce over time, suggesting that the effectiveness of this treatment will be long lasting.",
      "\tDevelop innovative approaches to pharmacological and surgical management\n\nInnovative approaches to managing obesity may lower certain barriers undermining treatment of both obesity and type 2 diabetes.For example, modulating the incretin axis may benefit both energy balance and glycemia.Novel pharmacological development may depend on information gained from more efficient use of genomic, proteomic, and metabolomic approaches and from information learned from studying weight-loss mechanisms in bariatric surgery.In addition, co-opting less traditional organs such as the brain and gut into the core pathophysiology of type 2 diabetes may reveal new biomarkers and/or targets for therapeutic intervention.Finally, safe and effective centrally acting drugs that decrease appetite or increase satiety are urgently needed.However, as regulatory agencies increase the need for safety testing, fewer new and innovative approaches for weight loss are being developed because of the prolonged time and immense expense involved.",
      "\t\n\nPharmacogenomic studies in case of newer therapies are few.Incretin-based therapies, which help control postmeal glucagon levels and hence blood sugar, involve the use of two types of medicine classes -DPP-4 inhibitors and GLP-1 receptor analogs.\t\n\nTable 2 summarizes some of the gene-drug interactions for a few important medicinal classes used in diabetes treatment.",
      "\tFuture developments in mostly untested areas\n\nBecause available treatments at present do not easily achieve and maintain normal concentrations of glucose as -cell function progressively decreases, new approaches are being developed (table 1), which represent mostly untested mechanisms.\t\n\nFigure 3: Drugs to treat type 2 diabetes (A) The rate of introduction of new classes of drugs has accelerated during the past 20 years.Two classes (animal insulin and inhaled insulin; red) are essentially no longer available as therapeutics. (B) Diff erent classes of drugs act on diff erent organ systems.Insulin is a replacement for the natural product of islet  cells.Classic organ systems that have been targeted for decades comprise the pancreatic islet, liver, muscle, and adipose tissue.Non-classic targets have been focused on recently, and include the intestine, kidneys, and brain.DPP4=dipeptidyl peptidase 4. SGLT2=sodium-glucose co-transporter 2. GLP-1=glucagon-like peptide 1.\t\n\nIn view of the fact that type 2 diabetes is a progressive disease due to advancing -cell dysfunction, can new drugs slow loss of -cell function to provide durable glucose control?In the ADOPT study, 161 recently diagnosed and previously untreated patients were given 4 years of monotherapy with glibenclamide, metformin, or rosiglitazone.Glibenclamide produced the largest initial reduction in glycaemia, but provided poorest maintenance of overall glucose control.Whereas the onset of glucose lowering with the other two drugs was slower than for glibenclamide, it was most sustained with rosiglitazone, with intermediate maintenance of glucose control with metformin, which was mostly related to eff ect on -cell function. 11,161Whether recently introduced drugs will maintain glucose control over the long term remains to be established.Limited data from a few patients suggest that incretin-based therapies, which are purported to improve -cell health, could have such a benefi t. 162 Strategies to slow disease progression have also focused on people with impaired glucose tolerance or impaired fasting glucose because of their high risk of development of type 2 diabetes.Several studies have examined the ability of lifestyle modifi cation and drugs to slow progression to diabetes (table 2). ][165][166][167][168][169][170][171][172][173][174][175] Findings from prolonged follow-up showed that in some instances the benefi t of treatment was retained for 10 years or more, [176][177][178] and could reduce risk of development of severe retinopathy. 179In the DPP study, 180 restoration of individuals to normal fasting and 2 h glucose concentrations only once during the intervention phase was associated with a reduced rate of subsequent diabetes, mostly as a result of improved -cell function.A question that has largely gone unanswered is whether the interventions actually alter the natural history of the disease, or simply mask the development of diabetes as a result of earlier commencement of treatment. 181Only reports of the eff ects of troglitazone in DPP 172 and insulin glargine in ORIGIN 146 suggest a residual benefi t after prolonged withdrawal of the intervention.However, despite good rationale for approval of interventions to delay the onset of diabetes, 182 no drug has yet received offi cial sanction as a preventive treatment.\tOral and injectable drugs: present knowledge, lessons learned, and implications for the future\n\nThe increasing prevalence of type 2 diabetes has stimulated development of many new approaches to safely treat hyperglycaemia (fi gure 3).The aim of these therapies is to reduce and maintain glucose concentrations as close to normal for as long as possible after diagnosis (panels 1, 2), and thereby prevent development of complications.Although some therapies have been unsuccessful because of adverse eff ects or negligible therapeutic effi cacy, several are very well accepted and are used worldwide.The mode of action for most of these drugs has been reported (fi gure 3).However, individual responses to these drugs can diff er greatly, probably as a result of the heterogeneous nature of the pathophysiology of type 2 diabetes.The appendix provides further discussion on drugs that have been widely available for more than a decade (eg, sulfonylurea antidiabetics, biguanide antidiabetics, -glucosidase inhibitors, and peroxisome proliferatoractivated receptor  agonists).",
      "\tPotential for treatment\n\nSuccessful glycaemic control of T2D patients often requires a combination of several of oral agents, together with subcutaneous insulin for more severe cases.The use of currently available therapeutics can often lead to side effects, including increase in body weight, risk of hypoglycaemia and gastrointestinal problems.In addition, the efficacy of these drugs is limited to the early stages of T2D, when fasting blood glucose levels are relatively low, with approximately 40% of T2D patients on oral anti-diabetics failing to control their blood glucose and having to supplement with insulin.And, of course, all T1D patients currently face a lifetime of injecting insulin.So there is room for more efficacious therapeutic agents.",
      "\tNanotechnology and Diabetes\n\nThe interface of nanotechnology in the treatment of diabetes has introduced novel strategies for glucose measurement and insulin delivery.Researchers have demonstrated the advantages of glucose sensors and closed-loop insulin delivery approaches in facilitating the diabetes treatment to make it [34] beneficial in both type 1 and type 2 diabetes.\t\n\nFor the management of type 2 diabetes, a well monitored glycemic control is required.The need to control the progressive deterioration of  cell function is essential since it can lead to a loss of glycemic control.Conventional drugs and insulin are effective but cannot repair the associated metabolic and glucoregulatory dysfunctions.The menace of diabetes is increasing day by day and aggressive and targeted combinational therapy is the need of the hour particularly incretin based therapy and peptide analogs.This may restore and preserve  cell function and halt the progression of type 2 diabetes [87].In the present era, the effectiveness and the success of the new drug will depend on its ability to treat/relieve one or more of the metabolic disturbances whether increased production of insulin or enhancement in glucose uptake and utilization by the peripheral tissues particularly skeletal muscle.Besides new generations of therapeutics, several other classes have also been reported as alternative strategies alone or in combinations to provide an effective treatment for diabetes.",
      "\tTherapeutics\n\nAside from insulin and insulin analogs, therapies for diabetes include those that enhance insulin secretion, those that stimulate insulin action, those that reduce hepatic and endogenous glucose production, and those that impact glycemia through other mechanisms.By better understanding the pathophysiology and natural history of various subtypes of diabetes and applying what we know about the modes of action and pharmacogenomics of existing therapies, we can better apply a personalized approach to diabetes management.There is a growing body of evidence regarding which phenotypic and genotypic subsets of patients with diabetes respond best, or are resistant to, specific therapies (113), including sulfonylureas (114,115), metformin (116,117), thiazolidinediones (118,119), incretin therapies (120), and inhibitors of sodium-glucose cotransporter 2 (SGLT2) (121,122).",
      "\t\n\nA variety of treatment modalities exist for individuals with type 2 diabetes mellitus (T2D).In addition to dietary and physical activity interventions, T2D is also treated pharmacologically with nine major classes of approved drugs.These medications include insulin and its analogues, sulfonylureas, biguanides, thiazolidinediones (TZDs), meglitinides, -glucosidase inhibitors, amylin analogues, incretin hormone mimetics, and dipeptidyl peptidase 4 (DPP4) inhibitors.Pharmacological treatment strategies for T2D are typically based on efficacy, yet favorable responses to such therapeutics are oftentimes variable and difficult to predict.Characterization of drug response is expected to substantially enhance our ability to provide patients with the most effective treatment strategy given their individual backgrounds, yet pharmacogenetic study of diabetes medications is still in its infancy.To date, major pharmacogenetic studies have focused on response to sulfonylureas, biguanides, and TZDs.Here, we provide a comprehensive review of pharmacogenetics investigations of these specific anti-diabetes medications.We focus not only on the results of these studies, but also on how experimental design, study sample issues, and definition of 'response' can significantly impact our interpretation of findings.Understanding the pharmacogenetics of anti-diabetes medications will provide critical baseline information for the development and implementation of genetic screening into therapeutic decision making, and lay the foundation for \"individualized medicine\" for patients with T2D.\t\nA variety of treatment modalities exist for individuals with type 2 diabetes mellitus (T2D).In addition to dietary and physical activity interventions, T2D is also treated pharmacologically with nine major classes of approved drugs.These medications include insulin and its analogues, sulfonylureas, biguanides, thiazolidinediones (TZDs), meglitinides, -glucosidase inhibitors, amylin analogues, incretin hormone mimetics, and dipeptidyl peptidase 4 (DPP4) inhibitors.Pharmacological treatment strategies for T2D are typically based on efficacy, yet favorable responses to such therapeutics are oftentimes variable and difficult to predict.Characterization of drug response is expected to substantially enhance our ability to provide patients with the most effective treatment strategy given their individual backgrounds, yet pharmacogenetic study of diabetes medications is still in its infancy.To date, major pharmacogenetic studies have focused on response to sulfonylureas, biguanides, and TZDs.Here, we provide a comprehensive review of pharmacogenetics investigations of these specific anti-diabetes medications.We focus not only on the results of these studies, but also on how experimental design, study sample issues, and definition of 'response' can significantly impact our interpretation of findings.Understanding the pharmacogenetics of anti-diabetes medications will provide critical baseline information for the development and implementation of genetic screening into therapeutic decision making, and lay the foundation for \"individualized medicine\" for patients with T2D.",
      "\t\n\ntherapeutic target for the development of agents to improve glucose regulation and to prevent or treat type 2 diabetes.",
      "\t\n\nThe only existing therapy is insulin for T1D.Developments in long-acting and glucose-sensitive insulins are improving the health and well-being of people with T1D, as are technological advances in continuous glucose monitoring devices, insulin pumps, closed-loop systems, and the artificial pancreas."
    ],
    [
      "\tGenetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor  (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a  cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18).",
      "\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.",
      "\tGenes and T2DM -from \"susceptibility\" to \"determination\"\n\nAs far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms [71,73] (Tab.1).Monogenic forms are a consequence of rare mutations in a single gene [73].Mutations may affect the structure and subsequently the function of a protein or tRNA.In some cases they may be localised in regulatory parts of genes and alter gene expression.Monogenic forms are characterised by high phenotypic penetrance, which means that the presence of the mutation practically determines the development of the disease.They are also characterised by early age of diagnosis, and frequently, but not always, a severe clinical picture, and occasionally the presence of extra-pancreatic features.Genetic background plays a critical role in their pathogenesis, while the environment only slightly modifies the clinical picture.The known forms of monogenic T2DM are characterized either by severe defect in insulin secretion or profound decrease in insulin sensitivity.Like in other Mendelian traits, in spite of their huge influence on the health of some individuals and families, their role in entire populations is very limited.\t\nThe development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two pathophysiological pathways and at least two groups of genes that may be involved in the pathogenesis of T2DM.As far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms.In this review, we present genes known to cause rare monogenic forms of diabetes with predominant insulin deficiency (MODY -maturity-onset diabetes of the young, MIDD -maternally inherited diabetes with deafness) and uncommon syndromes of severe insulin resistance.We also describe some of the main approaches used to identify genes involved in the more common forms of T2D and the reasons for the lack of spectacular success in this field.Although major genes for T2DM still await to be discovered, we have probably established a \"road map\" that we should follow.\t\n\nIn polygenic forms of T2DM, the susceptibility genetic variants have very modest consequence at the individual level, however, their population effects are significant [71,73,78].In case of polygenic diseases, we search for common variants that are present in the group of patients and in healthy controls.Those polymorphisms generate just a small increase in individual risk.For common diabetes forms caused by many genes and the environment the same strategies as described above were generally used however, with much less success.This fact is a result of fundamental differences in the character of the genetic background of both monogenic and complex forms.Many susceptibility genes for T2DM have been suggested but in majority of cases it is difficult to replicate the findings in other populations.One of the major problems in the search for genes responsible for common forms of diabetes is the genetic heterogeneity of the disease with different genes responsible for the development of T2DM in different populations.Furthermore, even within the same ethnic group, different genes may be responsible for different subtypes of diabetes (for instance with predominating failure in insulin secretion or insulin resistance).This is why several genome scans that have been completed so far are in general not fully reproducible [17,40,72].In addition to that, there are multiple methodological problems.Researchers were studying various populations differing in age of onset of diabetes, severity of clinical picture of the disease, and way of treatment of diabetes.In general, for the purpose of genome scans the researchers have to collect a large number of families (rather small in size-for example sibs) [47,71,73,76,78,89,119].In addition to that, analysis had different, often weak, statistical power and at the level of interpretation different criteria of significance were used.Some studies were based on the very strict criteria proposed by scientists from Massachusetts Institute of Technology while others were analysed with the usage of more liberal rules [57].This is why drawing more general conclusions based on these studies should be very careful.\t\n\nThe development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two pathophysiological pathways and at least two groups of genes that may be involved in the pathogenesis of T2DM.As far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms.In this review, we present genes known to cause rare monogenic forms of diabetes with predominant insulin deficiency (MODY -maturity-onset diabetes of the young, MIDD -maternally inherited diabetes with deafness) and uncommon syndromes of severe insulin resistance.We also describe some of the main approaches used to identify genes involved in the more common forms of T2D and the reasons for the lack of spectacular success in this field.Although major genes for T2DM still await to be discovered, we have probably established a \"road map\" that we should follow.",
      "\tII. Genetics of Type 2 Diabetes\n\nType 2 diabetes clearly represents a multifactorial disease, and several findings indicate that genetics is an important contributing factor.First, certain ethnic minorities and indigenous groups with low population admixture (e.g., Pima Indians, Micronesians and other Pacific Islanders, Australian Aborigines, and Mexican-Americans) show exceptionally high type 2 diabetes prevalence (up to 21% in Pima Indians) (10 -12).Second, type 2 diabetes clusters within families and first-degree relatives have, compared with the general population, an up to 3.5-fold higher risk to develop the disease (13,14).Finally, twin studies demonstrated a markedly higher concordance for type 2 diabetes in monozygotic compared with dizygotic twins (70 vs. 10%) (15).Type 2 diabetes does not follow simple Mendelian inheritance and, therefore, is considered a polygenic disease.According to the generally accepted common variant-common disease hypothesis (16), complex diseases, such as type 2 diabetes, are caused by the simultaneous occurrence of common DNA sequence variations (minor allele frequencies 5%) in many genes.Each of these DNA alterations is supposed to exert only moderate effects on the affected genes' function and/or expression, but in their sum, these variations confer an increased susceptibility toward the adverse environmental factors mentioned above.Single nucleotide polymorphisms (SNPs), exchanges of single base pairs, cover approximately 90% of the sequence variation within the human genome (SNP Fact Sheet of the Human Genome Project; available at http://www.ornl.gov/sci/techresources/Human_Genome/faq/snps.shtml) and are therefore regarded as the major determinants of the individual predisposition to complex diseases.Thus, strong efforts are currently ongoing to map and catalog these sequence variations (The International HapMap Project at http://www.hapmap.org/index.html.en).However, the less frequent copy number variations (due to deletion and/or duplication of DNA segments one kilobase to several megabases in size) and smaller DNA insertions, deletions, duplications, and inversions may also play a role.All of these findings initiated an intensive search for the genes, or better gene variants, responsible for the genetic predisposition to type 2 diabetes.",
      "\tDISCUSSION\n\nType 2 diabetes is a highly polygenic trait, and hundreds of loci associated with the disease have been identified, mostly via large GWAS meta-analyses conducted under additive genetic models (2,3).This prior work has produced useful results, identifying potential therapeutic targets and also enabling the creation of polygenic scores capable of quantifying one's genetic risk (34).A sizeable fraction of the heritability of type 2 diabetes, however, remains unexplained by loci identified using additive models.Recessive modeling offers a way to identify new associations, creating opportunities for discovery and improved genetic risk stratification.",
      "\tINTRODUCTION\n\nDiabetes is a common, chronic disease that profoundly impacts health and longevity.Susceptibility is influenced by inheritance, and there has been substantial progress in identifying genes which, when mutated, influence individual risk of disease.Through study of common and rare forms, both polygenic and monogenic, diabetes genetics encompasses many pressing issues in human genetic research.",
      "\t\n\nThe different types of heterogeneity at the phenotypic level are mirrored by potential different types of genetic heterogeneity.Thus, type 2 diabetes could be 'polygenic' as illustrated in Figure 1C, or it could be 'oligogenic' as illustrated in Figure 1D.Although there is no way to be certain about which pattern is correct, the many reports of linkages with substantial LOD scores (the ratio of the odds favoring vs the odds against linkage) between various chromosomal regions and type 2 diabetes (to be discussed below) are encouraging and favor the oligogenic pattern (Figure 1D).The uncertainties surrounding the issue of phenotypic and genetic heterogeneity are highly salient, since the strategies for gene discovery, the likelihood of success, and the public health relevance of the search for type 2 diabetes susceptibility genes are all profoundly dependent upon which of these types of heterogeneity turns out to be correct.\tThe Search for Diabetes Genes 111\n\n'polygenic', but rather 'oligogenic', i.e. that at least some diabetes susceptibility genes had relatively large effects.",
      "\tVariant classification\n\nKey to diagnosing monogenic diabetes and other genetic conditions is not only identifying the variant but also distinguishing The Journal of Clinical Investigation of occurrences leads to a higher level of evidence supporting pathogenicity.However, the uncommonness of monogenic diabetes often makes it difficult for individual laboratories to acquire enough cases.By pooling case data, expert panels can achieve levels of case-based evidence for pathogenicity not possible for any single laboratory or clinic.",
      "\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.",
      "\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\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\nIn this review, however, we focus on a different route from human genetics to translation, one that derives estimates of an individual's predisposition to diabetes and its subtypes (in the form of polygenic scores) from the patterns of individual geneticvariation at sites known to influence diabetes predisposition.\t\n\n During the last decade, there have been major advances in our understanding of the genetic basis of the most common subtypes of type 1 (T1D) and type 2 diabetes (T2D), with .500robust associations identified  Although individual variants typically have only a modest effect on risk, when combined into a polygenic score, they offer increasing power to capture information on individual patterns of disease predisposition with the potential to influence clinical management",
      "\t\nType 2 diabetes (T2D) had long been referred to as the ''geneticist's nightmare. ''Genome-wide association studies have fully confirmed the polygenic nature of T2D, demonstrating the role of many genes in T2D risk.The increasingly busier picture of T2D genetics is quite difficult to understand for the diabetes research community, which can create misunderstandings with geneticists, and can eventually limit both basic research and translational outcomes of these genetic discoveries.The present review wishes to lift the fog around genetics of T2D with the hope that it will foster integrated diabetes modeling approaches from genetic defects to personalized medicine."
    ],
    [
      "\t\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.",
      "\t\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients.",
      "\t\n\nIn addition, the mechanisms whereby a given DNA change leads to an increased risk of diabetes need to be reconstructed.In type 1 diabetes we need to understand how the susceptibility variants influence immune response and tolerance.In type 2, we need to know whether they influence disease predisposition through primary effects on beta cell function, through insulin action, or by some other mechanism.",
      "\t\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.\t\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.",
      "\t\n\nBy identifying key biological processes and genes involved in the pathogenesis of diabetes, novel drug targets for the disease and related metabolic disorders such as obesity and metabolic syndrome may be determined.",
      "\t\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner.",
      "\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\n\nRegardless, one expects many of the important susceptibility genes for type 2 diabetes will be uncovered in the next 10 years.Once that occurs, intense effort will be focused on developing targeted therapies.Also, medical care will shift to genetic testing of persons with type 2 diabetes, followed by giving them the most effective proven therapy for that genetic form of the disease.Also, their family members will undergo genetic testing while still normally glucose tolerant to determine if they carry a genetic predisposition.If so, specific treatment plans will be developed for prevention of the disease, again based on proven efficacy for each genetic defect.",
      "\t\n\nFailure to understand the pathophysiology of diseases such as type 2 diabetes and obesity frustrates efforts to develop improved therapeutic and preventive strategies.The identification of DNA variants influencing disease predisposition will, it is hoped, deliver clues to the processes involved in disease pathogenesis.This would not only spur translational innovation but also provide opportunities for personalized medicine through stratification according to an individual person's risk and more precise classification of the disease subtype.In this article, I consider the extent to which these objectives have been realized.",
      "\t\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.",
      "\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\nGreat strides have been made clinically in the prevention, development, and treatment of the disease but no therapeutic method have been completely successful till date.With new technologies revolutionizing the treatment possibilities, the search for an effective medication is not far ahead.The extensive research leading to the discovery of the pathway genes contributing to the development of the disease and the sequencing of complete genomes have revolutionized the diabetes research.The development of the techniques like the PCRs, DNA microarray, and gene knockouts with silencing has opened up a new area in the identification of the defective genes/mutations in the genome of the organism.The increasing prevalence of diabetes globally is creating a financial burden on the economy of the respective country.Unlike some other diseases, treatment exists for diabetes, and if managed correctly, it is very effective in reducing complications such as heart attacks, amputations, blindness, and kidney failure.With the ongoing research, a right therapeutic for the treatment of diabetes is not unachievable.",
      "\t\n\nThe future will see intensified research and improvement in such methodologies to identify and characterise the multiple genes underlying complex diseases.One of the most important goals of genetic studies of diabetes is to determine which multilocus genotypes (across all susceptibility loci) create the highest risk for development of diabetes.Individuals with those genotypes would be targeted for treatment to prevent diabetes when safe and effective prophylactic therapies become available.It is possible that several prophylactic options could be available, with effectiveness depending on the exact set of predisposing genes carried by the at-risk person.Thus, the next generation of genetic studies of Type I diabetes (and other complex disorders) will involve dissection of gene-gene interactions in order to clarify which persons, by virtue of their multilocus genotype, are most susceptible to diabetes.This research will be accompanied by studies of gene-environment interaction, when the relevant non-genetic factors are more clearly understood (eg.do differences in diabetes susceptibility via antiviral defence genes relate primarily to certain types of virus? ).",
      "\tConclusions\n\nHow will sequencing genomes influence the health of people at risk for or affected with diabetes?The more complete understanding of the biological mechanisms underlying diabetes derived from these studies may lead to identification of novel drug targets.Individuals with variants in genes responsible for MODY or neonatal diabetes respond better to specific drugs [50,51], and sequencing may identify small numbers of individuals with combinations of rarer, more highly penetrant variants that respond better to specific therapeutic options.Although sets of known variants for type 2 diabetes do not add substantially to prediction of type 2 diabetes development in the overall population [52,53], identification of individuals at greater or lower genetic risk for diabetes within the overall population or in specific subgroups, such as younger onset or leaner individuals [54,55], could lead to better targeted health information and also allow identification of higher risk individuals leading to more efficient design of clinical trials for disease prevention.\t\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.",
      "\t\n\nAll very well, you may say, that must be great for the geneticists, but what does all of this mean for our understanding of diabetes?And what difference will this make to the clinical management of this condition?In truth, it is far too early to offer an authoritative answer to such questions, but here are three immediate lessons.",
      "\tFUTURE PROSPECTIVES\n\nRecent technological developments around CRISPR-Cas9 and its derivative technologies, combined with advances in human cellular models, should accelerate our understanding of the interplay between diabetes risk-associated genetic variants and their functional roles in disease pathogenesis.These approaches may also find use in clinical applications and in drug screens (Figure 2), enhancing the development of precision medicines for personalized treatment.",
      "\t\n\nT2D human genetics has other potentials; for instance, NGS large screen for mutations in new putative drug targets can be very helpful in validating them (for example, if causing insulin secretion defects in beta cells) and in determining drugs side effect in humans carrying loss-or gain-of-function mutations.T2D genetics has shown biologists the results of the experiments of nature that have mutated genes and non-coding regions, with direct effects on T2D risk.It revealed in human the role of key pathways involved in glucose homeostasis, opening novel therapeutic avenues.As modern genomics is not biased by prior researchers' hypotheses, but aims to assess all genome influences comprehensively, it brings unique integrated information that deserves to be tested by experimental biologists in animal models and cell lines.Biologists ought not to fear the plethora of T2D genes.On the contrary, together with geneticists, they should formulate innovative strategies for exploiting genetic discoveries in order to answer the following question in humans: among the numerous described metabolic pathways, what is essential to glucose control?What is causing early diabetic abnormalities?And what should be targeted to prevent or even cure T2D?",
      "\tTHE GENETICS OF TYPE 1 DIABETES\n\nThe study of the genome to map disease-susceptibility regions for T1D and other multifactorial diseases has been facilitated by recent advances in next generation DNA sequencing methods."
    ]
  ],
  "task_id": [
    {
      "task_id": "4EE84E22AA592E8150F75B2C3540A23C"
    },
    {
      "task_id": "0B617CD2F3796318CCFF8D220E037B5A"
    },
    {
      "task_id": "D616F7F573665700708E48DD48AA4683"
    },
    {
      "task_id": "D4DBF70BD6F78E8A5C515706F4892B1E"
    },
    {
      "task_id": "0EB12EE3B8B38A98C5E6ACA63D55A150"
    }
  ]
}