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{
"question": [
"what are confounding factors in diabetes?",
"How is the immune system related to diabetes?",
"What are the genomic variants associated with immune system components and diabetes?",
"What is the role of the immune system in the metabolomics of diabetes and associated conditions?",
"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?"
],
"answer": [
"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.",
"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.",
"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.",
"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.",
"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes."
],
"contexts": [
[
"A wide array of other dietary compounds and environmental triggers have been shown to affect diabetes development in animal models, and for some of these such as omega-3 fatty acids (312), there is limited proof in human patients.",
"Type 2 diabetes (T2D) is a result of complex gene-environment interactions, and several risk factors have been identified, including age, family history, diet, sedentary lifestyle and obesity.Statistical models that combine known risk factors for T2D can partly identify individuals at high risk of developing the disease.However, these studies have so far indicated that human genetics contributes little to the models, whereas socio-demographic and environmental factors have greater influence 1 .Recent evidence suggests the importance of the gut microbiota as an environmental factor, and an altered gut microbiota has been linked to metabolic diseases including obesity 2,3 , diabetes 4 and cardiovascular disease 5 .",
"Dietary factors [source]Reduced risk Mediterranean diet pattern [130] Fruit and vegetable intake [131] Fermented dairy products [132] Fatty fish intake [133] Tea intake [134] Elevated risk Red and processed meat intake [135] Sweetened beverages [136] Null association Total dairy products or milk intake [132] Total fish intake [133] Dietary energy density [137] Carbohydrate intake [138] a Further information about the InterAct project can be found at www.inter-act.eu.There are also other forthcoming publications on dietary factors and the risk of diabetes.cohort studies also found an increased diabetes incidence among passive smokers [142].Finally, in-utero exposure to maternal smoking is associated with overweight and obesity which may predispose to diabetes and other metabolic disturbances in the offspring [143].Psychosocial factors encompass two broad areas which are more closely related to socioeconomic status or to psychological/psychiatric factors.Within the InterAct study, people who had a lower educational level had a 70% higher relative risk for diabetes, which remained at around 40% even after adjustment for differences in obesity [144].The association between emotional stress, job strain, anxiety and depressive disorders and increased incidence of type 2 diabetes is less well-established, but recent data [145][146][147] strongly indicate that this area merits further study to better understand the relationship between these potential risk factors.",
"It isplausible that such factors may also operate at the very beginning of the humanlifecourse but their identity, and the environmental factors they synergize with,remain unknown (Bloomfield et al 2006), awaiting discovery. Chaufan also makes a strong case that inequalities in the provision of healthcare and education are compounding the growing problem of type 2 diabetes inthe developed (and increasingly, less developed) nations today (Chaufan 2007). This is an important point, and one with which we agree, but it is concerned primarily with issues about resource allocation and distributive justice.Type 2 diabetes mellitus as an illustrative exampleThe persuasiveness of Chaufans argument comes from her dependence on type 2diabetes as her main illustrative example. It is true that environmental factors canaccount for up to 8090% of the population attributable risk for this condition(Cooper & Psaty 2003), and it may be that in a profoundly diabetogenic environment such as exists in many 21st century developed countries, knowing about G E interactions adds little per se to the management of an overweight and inactivepopulation.",
"Understanding risk factors for diabetes is therefore critical to its early diagnosis.Key risk factors for diabetes include obesity (Mokdad et al. 2001;Must et al. 1999) and prediabetes.A fasting blood sugar well into the \"reference range\" has been shown to be a risk factor for diabetes (Tirosh et al. 2005).Indeed, we have shown that the 4-year risk of diabetes among participants in the FHS with prediabetes ranges from a 12.7-fold increase (in men) to a 22.3fold increase (in women) (Levitzky et al. 2008).The metabolic syndrome, a constellation of metabolic risk factors that have been observed to cluster with each other more than would be expected by chance (Meigs et al. 1997), was formally acknowledged as a syndrome involving the fulfillment of at least 3 criteria, including elevated waist circumference, impaired fasting glucose, elevated blood sugar, elevated triglycerides, or low high-density lipoprotein cholesterol (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults 2001).The presence of the metabolic syndrome is a strong risk factor for the subsequent development of diabetes, conferring a nearly 7-fold increased risk among those with as compared with those without the metabolic syndrome (Wilson et al. 2005).As a means of better trying to identify who is at early risk for diabetes, a prediction equation for incident diabetes was developed in the FHS (Wilson et al. 2007).A \"simple clinical model\" was derived, which includes parental history of diabetes, obesity, hypertension, low high-density lipoprotein cholesterol, elevated triglyceride levels, and impaired fasting glucose; the c-statistic for this model was robust at 0.85.Importantly, more complex models with variables such as waist circumference, insulin resistance, 2-hour postprandial glucose derived from an oral glucose tolerance test, and C-reactive protein were not independent predictors of diabetes.This prediction model highlights how simple clinical variables that are readily available can be used to identify individuals at high risk for developing diabetes even before they have evidence of the disease.In aggregate, these findings from the FHS make several important points.First, the incidence rate of diabetes is increasing.Second, because the relative risk of diabetes as a CVD risk factor has remained constant over time, the relative importance of diabetes with respect to CVD has increased.Finally, individuals with diabetes remain inadequately managed with regard to CVD risk factor levels.These findings highlight the importance of early identification of diabetes and a means to identify diabetes early in the life course to promote the early aggressive management of CVD risk factors.Another major remaining question is why the relative risk for diabetes as a CVD risk factor has failed to decrease over time.As described earlier, the rates of CVD among participants in the FHS have decreased; but this reduction has been outpaced by those without diabetes (Fox et al. 2004a).In terms of primary prevention, we can aim to reduce the burden of uncontrolled CVD risk factors, including incompletely treated hypertension, dyslipidemia, and participants with diabetes who continue to smoke (Preis et al. 2009a).Observational studies such as the FHS can help to explore rates of treatment and control for known modifiable risk factors.",
"DietExcessive caloric intake is a major driving force behind escalating obesity and type 2 diabetes epidemics worldwide, but diet quality also has independent effects.In the Nurses' Health Study (NHS), we found that the quality of fats and carbohydrates play an important role in the development of diabetes, independent of BMI and other risk factors (11).In particular, higher dietary glycemic load (GL) and trans fat are associated with increased diabetes risk, whereas greater consumption of cereal fiber and polyunsaturated fat is associated with decreased risk (Fig. 2).In a meta-analysis, we found that a 2 serving/day increment in whole-grain intake was associated with a 21% lower risk of diabetes (12).",
"IntroductionThe aetiology of type 2 diabetes is poorly defined: several studies indicate that the disease results from a combination of genetic susceptibility and external risk factors [1].According to this multifactorial model, genetically predisposed subjects will not necessarily develop overt disease unless they are also exposed to particular environmental factors [2].Important risk factors for the development of type 2 diabetes include a family history of diabetes, increased age, hypertension, lack of physical exercise, and obesity [1].",
"Environmental factors such as age, weight gain, excessive energy intake, physical inactivity and inheritance of genes predisposing to insulin resistance are major risk factors for development of T2D.Nutrient imbalances such as deficiency of vitamin D [19] and increased iron absorption and storage in the body [20,21], changes in gut microbiota [22] and exposure to pollutants [23] may confer risk for development of T2D.Early-life or intrauterine environment [24] and epigenetics [25] also play a role in conferring susceptibility to diabetes.Obstructive sleep apnea, which is associated with obesity, insulin resistance and glucose intolerance, also contributes to the pathology of T2D [26].",
"What these predisposing factors share is an ability to negatively impact the glucose homeostasis system through worsening of insulin resistance or to impair b-cell function.Superimposing these factors onto a genetically compromised glucose homeostasis system raises the risk of progressing to hyperglycemia.It is the rapid emergence of these disadvantageous environmental factors that is causing the worldwide diabetes epidemic.This concept of environmental changes promoting diabetes was highlighted many years ago by populations that rarely experienced type 2 diabetes, but then moved from a nomadic or farm existence to urban environments followed by an explosion of diabetes, typically with profound obesity: Pima Indians in the Southwest U.S., Saharan nomadic tribes, Australian Aborigines, and many others.Particularly dramatic were studies that showed reversal of the diabetes when they returned to their prior way of life (15).A recent example of this is the rapidly rising incidence of type 2 diabetes in China and India as people move from the country to cities-there is a 0.1-0.2%incidence of diabetes for rural farmers in China as opposed to well more than 5% for city dwellers.Perhaps the scariest example of this is children in the U.S. where the obesity statistics worsen yearly.As many as 20% of U.S. children are now obese, and they are developing all of the elements of the metabolic syndrome-insulin resistance, hypertension, hyperlipidemia, and glucose intolerance (16).",
"Taken together, non-invasive risk factors including age, sex, BMI, waist circumference, family history, smoking or hypertension form the basis of all diabetes risk scores.Routine clinical biomarkers, such as glucose, HbA 1c , lipids and uric acid, have the potential to improve the predictive ability of these basic risk factors, but AROCs rarely exceed 0.85.This argues in favour of a search for novel risk factors to further improve the accuracy of diabetes risk models.",
"There are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9].",
"In multivariate analyses (Table 3), diabetes was related to a higher risk of all-cause MCI even after adjusting for age, sex, ethnic group, years of education, APOE 4, hypertension, low-density lipoprotein level, heart disease, stroke, and current smoking (HR, 1.4; 95% confidence interval [CI], 1.1-1.8).",
"Clinical Factors Predicting Incidence of DiabetesIn both the MPP and Botnia studies, a family history of diabetes, an increased BMI, and increased levels of blood pressure and serum levels of triglycerides, apolipoprotein A-I, and liver enzymes were independent predictors of future type 2 diabetes (Table 1).In the MPP study, current smoking was also associated with a marked increase in the risk of diabetes.Impaired insulin secretion and action, particularly insulin secretion adjusted for insulin resistance (disposition index), were strong predictors of future diabetes.The presence of a first-degree family history of diabetes doubled the risk of the disease that was seen with an increased BMI (Fig. 2A) and a low disposition index (Fig. 2B).",
"The worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed.The worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed.",
"In sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved.",
"Aetiological factorsProspective studies suggest that the main pathophysiological defects leading to type 2 diabetes are insulin resistance and a relative insulin secretory defect.The main aetiological risk factors are age, obesity, family history, and physical inactivity.Dietary risk factors have recently emerged: risk is increased by high consumption of red and processed meat 13 and sugar-sweetened beverages, 14 and reduced by intake of fruit and vegetables, 15 some types of dairy products, 16 and some overall dietary patterns. 17Novel strategies to use quantifiable nutritional biomarkers are paving the way for more detailed understanding of the association between diet and diabetes.Although the heritability of type 2 diabetes is high (30e70%) and more than 60 genetic variants related with diabetes risk have now been identified, 18 even when combined into a genetic score, known genes contribute little to the prediction of diabetes.Phenotype-based risk models provide greater discrimination for diabetes, and the addition of genotypic information adds no more than 5e10% improvement in prediction.The current conclusion is that genetic variants provide insights into biological pathways and pathogenesis of diabetes, but not its prediction.It is likely that interactions between the environment/lifestyle and genetic factors provide the explanation for the risk of type 2 diabetes, but demonstrating such interaction is challenging.Encouraging research findings have recently shown higher absolute risk of diabetes associated with obesity at any level of genetic risk. 19evention and screening"
],
[
"V. IMMUNE EVENTS IN TYPE 1 DIABETESSeveral silent immune events occur before the clinical symptoms of type 1 diabetes become apparent.Most importantly, autoantibodies are produced and self-reactive lymphocytes become activated and infiltrate the pancreas to destroy the insulin-producing beta-cells in the islets of Langerhans (56).This persistent, targeted destruction may go undetected for many years, and the first clinical symptoms only become apparent after a majority of the beta-cells have been destroyed or rendered dysfunctional, making the individual dependent on insulin for survival (Fig. 2).Therefore, high priority is given to the search for \"biomarkers\" as whistleblowers of an ongoing autoimmune response.We will highlight some important immunological events here.Additional information on immune cell cross-talk in T1D can be found elsewhere (243).",
"IntroductionType 1 diabetes (T1D) results from immune-mediated selective destruction of pancreatic islet cells resulting in insulin deficiency and hyperglycemia [1,2].Symptoms of polydipsia, polyuria, polyphagia and weight loss manifest when significant numbers of islet cells have been destroyed.However, antibodies to islet autoantigens can be detected in peripheral blood prior to clinical disease [1,3].With early diagnosis of disease or assessment of risk, immune therapy may impede islet destruction and preserve insulin production, delaying onset of clinical manifestations [2].",
"Background: The immune system matures mainly during the postnatal period through breastfeeding, and is partly modified by nutritive factors.The manner by which early feeding practices influence the development of type 1 diabetes mellitus (TID) is not clear.Also the use of genetics in prognostic evaluation of the disease has not be studied intensely.",
"Figure 1-Schematic of the pathogenesis of diabetes.Genetic and environmental factors, acting via complex immunological mechanisms, result in b-cell destruction that leads to type 1 diabetes.Gene-environment interactions also underlie susceptibility to type 2 diabetes, the pathophysiological hallmarks of which include insulin resistance and b-cell dysfunction.",
"The results revealed that a major type of immune actors known as T cells are under the control of genetic factors associated with type 1 diabetes susceptibility.For instance, a specific type of T cells showed shared genetic control with type 1 diabetes.In addition, 15 loci were identified that influenced immune responses in the patients.Among those, 12 have never been reported to be involved in immune responses in healthy people, implying that these regions might only regulate the immune system of individuals with type 1 diabetes and other similar disorders.Finally, Chu, Janssen, Koenen et al. propose 11 genes within the identified loci as potential targets for new diabetes medication.These results represent an important resource for researchers exploring the genetic and immune basis of type 1 diabetes, and they could open new avenues for drug development.Many studies have highlighted the role of environmental, genetical, and immunological factors in the pathogenesis of T1D (Pociot and Lernmark, 2016;Rewers and Ludvigsson, 2016).Environmental factors such as being overweight, infections, microbiome composition, and dietary deficiencies have been reported as risk factors for T1D (Rewers and Ludvigsson, 2016).In turn, the immunological pathogenesis (Cabrera et al., 2016) of T1D includes innate inflammation and adaptive immunity, such as enhanced T cell responses (Hundhausen et al., 2016).In the last two decades, large genome-wide association studies (GWAS) performed have underscored the contribution of genetic polymorphisms to T1D for the susceptibility, with ~60 genomic loci associated with T1D risk identified (Barrett et al., 2009;Bradfield et al., 2011;Cooper et al., 2008;Grant et al., 2009;Huang et al., 2012;Onengut-Gumuscu et al., 2015;Ram et al., 2016).While these loci show significant enrichment in specific immune-related biological pathways, such as cytokine signaling and T cell activation (Barrett et al., 2009;Cooper et al., 2008), the functional consequences of many of these loci and genetic variants are still unknown.We thus lack information that could link the genetic susceptibility factors to the immunological pathways potentially important for T1D pathogenesis.The genetically regulated inflammatory response signature in T1D may also be relevant for the inflammatory response in general and may become modified by the chronic hyperglycemic state.The composition and activity of the human immune system is under genetic control, and people with certain changes in their genes are more susceptible than others to develop type 1 diabetes.Previous studies have identified around 60 locations in the human DNA (known as loci) associated with the condition, but it remains unclear how these loci influence the immune system and whether diabetes will emerge.Interrelationship between immune-cell counts and cytokine production in T1DWe collected blood samples from 243 T1D patients (300DM cohort), following a previously described methodology (Aguirre-Gamboa et al., 2016;Ter Horst et al., 2016;Li et al., 2016).The baseline characteristics of the 300DM and a cohort of healthy individuals (500FG) are shown in Supplementary file 1B.Their median age was 53.5 years (range 20-85), and they had a median diabetes duration of 28 years (range 1-71 years).Hence, the cohort generally consisted of middle-aged people with long-standing T1D.We measured 72 types of immune cells covering both lymphocytes and monocyte lineages and 10/6 (300DM/500FG) different cytokines released in response to stimulation with four types of human pathogens in both cohorts (Figure 1A).Background: The large inter-individual variability in immune-cell composition and function determines immune responses in general and susceptibility o immune-mediated diseases in particular.While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown.Methods: Blood samples were collected from 243 patients with T1D of Dutch descent.We applied genetic association analysis on >200 immune-cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls.Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition.Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation.Genome-wide quantitative trait loci (QTLs) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation.Conclusions: This study provides new insights into the genetic factors that affect immunological responses in T1D.Background: The large inter-individual variability in immune-cell composition and function determines immune responses in general and susceptibility o immune-mediated diseases in particular.While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown.Methods: Blood samples were collected from 243 patients with T1D of Dutch descent.We applied genetic association analysis on >200 immune-cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls.Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition.Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation.Genome-wide quantitative trait loci (QTLs) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation.Conclusions: This study provides new insights into the genetic factors that affect immunological responses in T1D.",
"Type 2 diabetes is characterized by the failure of the -cells to compensate for peripheral insulin resistance (6).Within the last decade, an increasing body of evidence has accumulated in favor of a putative role of immuno-related mechanisms and factors in the pathogenesis of type 2 diabetes, both with regard to the progressive -cell failure and destruction and to the peripheral insulin resistance (2,3).",
"T1DM pathogenesis involves innate and adaptive immune activity (13) coupled with failures in central and peripheral tolerance mechanisms that enable expansion of disease-mediating autoreactive T cells (14).Other immune cells are also involved, including B cells, as evidenced by the development of autoantibodies that precede clinical onset in almost all patients (15).Chemokines and cytokines are involved in T1DM pathogenesis by influencing immune activity, impairing -cell function, and inducing -cell death (16,17).",
"If the pathogenesis of diabetes begins in very early life (perhaps even prenatally), then the immune status of the mother during pregnancy could be as relevant as the immune status of her diabetes-at-risk offspring.If so, then elucidating the genetic basis of Type I diabetes will also require analysis of maternal genotype and maternal-fetal genotype interactions.Very few studies of this nature have been conducted.Furthermore, if viral infection is involved in the initiation of the autoimmune process, then genetic differences between individuals in immune response towards viruses could alter their predisposition to Type I diabetes.",
"Figure 1-Genetic and environmental risk factors impact inflammation, autoimmunity, and metabolic stress.These states affect b-cell mass and/or function such that insulin levels are eventually unable to respond sufficiently to insulin demands, leading to hyperglycemia levels sufficient to diagnose diabetes.In some cases, genetic and environmental risk factors and gene-environment interactions can directly impact b-cell mass and/or function.Regardless of the pathophysiology of diabetes, chronic high blood glucose levels are associated with microvascular and macrovascular complications that increase morbidity and mortality for people with diabetes.This model positions b-cell destruction and/or dysfunction as the necessary common factor to all forms of diabetes.Among the environmental associations linked to type 1 diabetes are enteroviral and other infections (51,52) and altered intestinal microbiome composition (53).The timing of exposure to foods including cereal (54) and nutrients such as gluten ( 55) may influence b-cell autoimmunity.Low serum concentrations of vitamin D have been linked to type 1 diabetes.Perinatal risk factors and toxic doses of nitrosamine compounds have been implicated in the genesis of diabetes.",
"In type 1 diabetes, the autoimmune destruction of cells by the cellular and humoral immune system in the pancreatic islets of Langerhans leads to impaired insulin secretion and subsequently to hyperglycemia.This type of diabetes is characterized by the appearance of antigen-specific T cells and antibodies in peripheral blood which are directed against a variety of -cell antigens including glutamic acid decarboxylase, tyrosine phosphatase IA-2, a zinc transporter and insulin.The onset of type 1 diabetes frequently occurs before 20 years of age, but disease manifestation is also common in adult patients.Exogenous administration of insulin is necessary to maintain glucose homeostasis and to prevent early and late diabetic complications [32,36].In type 2 diabetes, comprising approximately 90% of the cases of diabetes mellitus, hyperglycemia is the consequence of a relative insulin deficiency and insulin resistance of various tissues including muscle and adipose tissue.While in early type 2 diabetes, insulin resistance and the resulting increased metabolic demand may be overcome by increased pancreatic insulin secretion, failure of cells to maintain adequate insulin production and a decrease in -cell mass are common in progressive disease, resulting in chronic hyperglycemia and loss of metabolic control [33,37,38].Hyperinsulinemia is associated with down-regulation of insulin receptors, thus further contributing to the exhaustion of insulin production in cells [39].Overweight and obesity are significant risk factors for type 2 diabetes, which is increasing as a consequence of the Western lifestyle.Hence, diabetes is expected to become be an even greater health problem in the future deserving further attention [33,37].",
"Brief Genetics ReportT ype 1 diabetes results from an immune-mediated destruction of insulin-producing -cells in the pancreatic islets of Langerhans.The activation of autoreactive lymphocytes and the cytokineinduced apoptosis of pancreatic -cells play a major role in the etiology of type 1 diabetes.1,25-Dihydroxyvitamin D 3 [ 1 , 2 5 ( O H ) 2 D 3 ] inhibits lymphocyte activation and affects other elements of the immune system, such as cytokine and immunoglobulin production, as well as major histocompatibility complex (MHC) class II and cluster of differentiation (CD)-4 expression (1).In NOD mice, the development of diabetes can be prevented by administration of 1,25(OH) 2 D 3 ( 2 ) , which inhibits lymphocyte activation and restores the altered ratio of CD4/CD8 cells.",
"Type 1 diabetes is an autoimmune disorder afflicting millions of people worldwide.Once diagnosed, patients require lifelong insulin treatment and can experience numerous disease-associated complications.The last decade has seen tremendous advances in elucidating the causes and treatment of the disease based on extensive research both in rodent models of spontaneous diabetes and in humans.Integrating these advances has led to the recognition that the balance between regulatory and effector T cells determines disease risk, timing of disease activation, and disease tempo.Here we describe current progress, the challenges ahead and the new interventions that are being tested to address the unmet need for preventative or curative therapies.",
"The immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic cells, which greatly increases their risk for Type-1 diabetes.An environmental factor under active investigation is the gut microbiome due to its important role in immune system education.",
"At clinical onset (stage 3), celltargeted auto immunity is likely to have occurred for a prolonged period, as indicated by the presence of CD4 + and CD8 + T cells, dendritic cells, macrophages and B cells in and around the islets of Langerhans in many, but not all, patients with newly diagnosed T1DM 2,104 .These data are based on observations from samples obtained at disease onset by fineneedle biopsy 105 or by highrisk minimal pancreatic tail resection 106 , and they have con firmed previous data from pancreatic tissue samples from individ uals who have succumbed to diabetic keto acidosis (that is, acidosis due to the breakdown of lipids to ketones as an alternative source of glucose) 2,107,108 .In this setting, the inflammatory lesion does not affect all islets, and the insulitis process is patchy.Importantly, the volume or mass of islet cells producing gluca gon, somato statin or pancreatic polypeptide remains unaffected at the clinical onset of T1DM 2,104 .At present, there is no explan ation of why the cells and not the cells that produce glucagon, somatostatin or pancreatic polypeptide are attacked by the immune system.Separate auto antibodies that target human pancreatic cells prod ucing glucagon and those that produce somatostatin have been found in some patients, but further studies of these potentially unique patients are needed 109 ."
],
[
"In 2008, to increase the power of identifying variants with modest effects, a meta-analysis of three GWAS, including Diabetes Genetics Initiative (DGI), Finland-United States Investigation of NIDDM Genetics (FUSION), and Wellcome Trust Case Control Consortium (WTCCC), were conducted.This study detected at least six previously unknown loci that reached genome-wide significance for association with T2D ( < 5 10 8 ), with the loci being JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2 [19].Genetic variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, and THADA have been reported to affect pancreatic -cell functions [59,60].",
", for the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 9Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D) [1][2][3][4][5][6][7][8][9][10][11] .Established associations to common and rare variants explain only a small proportion of the heritability of T2D.As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and B2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975.We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P 5.0 10 -14 ), CDC123-CAMK1D (P 1.2 10 -10 ), TSPAN8-LGR5 (P 1.1 10 -9 ), THADA (P 1.1 10 -9 ), ADAMTS9 (P 1.2 10 -8 ) and NOTCH2 (P 4.1 10 -8 ) gene regions.Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.",
"Results from genome-wide association studies (GWAS) of type 1 diabetes (T1D) (Barrett et al., 2009), T2D (reviewed in Prokopenko et al., 2008), and related metabolic traits (Dupuis et al., 2010;Ingelsson et al., 2010;Prokopenko et al., 2009) suggest that genetic variation in cis-regulatory elements may play an important role in b cell (dys)function and diabetes susceptibility (De Silva and Frayling, 2010).Of the 18 most strongly associated single-nucleotide polymorphisms (SNPs) in each of the T2D-associated loci, only 3 are missense variants; the remaining are noncoding (Prokopenko et al., 2008).Furthermore, there is evidence for allele-specific effects of two T2Dassociated SNPs on the islet expression level of nearby genes (TCF7L2 [Lyssenko et al., 2007] and MTNR1B [Lyssenko et al., 2009]).However, the dearth of annotation of functional regulatory elements has limited the capacity to investigate the role of regulatory variation in complex diseases such as T2D.",
"Genetic studies of type diabetes (TD) have identified 50 susceptibility regions ,2 , finding major pathways contributing to risk 3 , with some loci shared across immune disorders 4-6 .To make genetic comparisons across autoimmune disorders as informative as possible, a dense genotyping array, the Immunochip, was developed, from which we identified four new TD-associated regions (P < 5 0 8 ).A comparative analysis with 5 immune diseases showed that TD is more similar genetically to other autoantibody-positive diseases, significantly most similar to juvenile idiopathic arthritis and significantly least similar to ulcerative colitis, and provided support for three additional new TD risk loci.Using a Bayesian approach, we defined credible sets for the TD-associated SNPs.The associated SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34 + stem cells.Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal.T1D results from the autoimmune destruction of pancreatic cells, leading to absolute dependence on exogenous insulin to regulate blood glucose levels 7 .In the present study, we designed and used the Immunochip, a custom Illumina Infinium high-density genotyping array, to (i) identify additional risk loci for T1D, (ii) refine mapping of T1D risk loci to their sets of most associated credible SNPs in order to (iii) analyze the locations of the credible SNPs with respect to regulatory sequences in tissues and cell types, and (iv) assemble summary genome-wide association study (GWAS) and Immunochip results from multiple immune diseases to allow comparisons of the genetic risk profiles of these diseases.The T1D SNP and indel content selected for inclusion on the Immunochip was chosen on the basis of the 41 T1D-associated regions known at the time (February 2010) 1 and 3,000 'wildcard' SNPs that tagged candidate genes or other SNPs with suggestive evidence of association (5 10 8 < P < 1 10 5 ) from GWAS of T1D.In parallel, we collected and curated all available association results for immune diseases for which the Immunochip was designed.To allow efficient comparison and downstream analysis by the research community, we created a publicly available, integrated, web-based portal (ImmunoBase; see URLs) containing complete association summary statistics that are available for querying, browsing or bulk download.",
"Impact of T1D GWAS SNPs on immune phenotypes in T1D patientsConsidering that T1D is a multifactorial disease with a genetic component, we tested whether the known risk variants of T1D affect immune phenotypes and function.We first checked SNPs within the HLA locus in our association studies on cell proportion and cytokine production level.Consistent with our previous findings in 500FG, we did not observe any significant associations of HLA allelic variants in 300DM.We then acquired non-HLA genetic loci from published GWAS of European background were acquired from the GWAS-catalog (November 2019) (Buniello et al., 2019).Among these, genetic variants in 63 independent T1D loci were present in our data, and we found that 13 of these 63 were indeed associated with susceptibility to T1D with nominal significance (p-value < 0.05) (Supplementary file 1C).Figure 2. Impact of type 1 diabetes (T1D) genome-wide association studies (GWAS) single-nucleotide polymorphisms (SNPs) on immune phenotypes. (A) Quantile-quantile (Q-Q) plots of quantitative trait locus (QTL) profiles of 62 T1D GWAS loci grouped by cell populations.The distribution of p-values of associations with T cells traits (blue) shows a significant deviation from an expected uniform distribution (dashed line). (B) Histogram showing number of associations observed (red line) and those in permutations (blue bars). (C) Heatmap of QTL profiles of cell proportion carrying certain chemokine receptors across 62 T1D GWAS loci, colored by log10(p-values) and effect direction of the T1D risk allele.Arrowhead indicates a T1D risk allele rs11574435-T.The online version of this article includes the following figure supplement(s) for figure 2: Figure supplement 1. Qqplots of QTL profiles of 62 T1D GWAS loci grouped by cytokine types.We next investigated whether these genetic risk loci for T1D affect immune parameters and function.The quantile-quantile plot of the association of the 63 T1D GWAS loci with different cell types and cytokines illustrates an inflated deviation from an expected uniform distribution (Figure 2A, Figure 2-figure supplement 1).We further tested whether this deviation can be explained by chance by comparing the association of immune traits with T1D GWAS SNPs with that of 1000 randomly selected independent SNPs (Figure 2B, Materials and methods).The p-value shows that the T1D GWAS SNPs are enriched in association with T cell traits in the T1D cohort (p-value = 0.007).",
"Table 1Polymorphisms in the human genome associated with type 1 diabetes (Adapted from (Ram et al., 2016b)).The genetic polymorphism data (i.e.SNPs) has been associated with T1D using genome-wide association studies and meta-analyses (references as noted).SNP, single nucleotide polymorphism.",
"Recent large genome-wide association studies (GWAS) have identified multiple loci which harbor genetic variants associated with type 2 diabetes mellitus (T2D), many of which encode proteins not previously suspected to be involved in the pathogenesis of T2D.Most GWAS for T2D have focused on populations of European descent, and GWAS conducted in other populations with different ancestry offer a unique opportunity to study the genetic architecture of T2D.We performed genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945 controls), 2,034 Malays (794 cases, 1,240 controls), and 2,146 Asian Indians (977 cases, 1,169 controls).In addition to the search for novel variants implicated in T2D, these multi-ethnic cohorts serve to assess the transferability and relevance of the previous findings from European descent populations in the three major ethnic populations of Asia, comprising half of the world's population.Of the SNPs associated with T2D in previous GWAS, only variants at CDKAL1 and HHEX/IDE/KIF11 showed the strongest association with T2D in the meta-analysis including all three ethnic groups.However, consistent direction of effect was observed for many of the other SNPs in our study and in those carried out in European populations.Close examination of the associations at both the CDKAL1 and HHEX/IDE/KIF11 loci provided some evidence of locus and allelic heterogeneity in relation to the associations with T2D.We also detected variation in linkage disequilibrium between populations for most of these loci that have been previously identified.These factors, combined with limited statistical power, may contribute to the failure to detect associations across populations of diverse ethnicity.These findings highlight the value of surveying across diverse racial/ethnic groups towards the fine-mapping efforts for the casual variants and also of the search for variants, which may be population-specific.Recent large genome-wide association studies (GWAS) have identified multiple loci which harbor genetic variants associated with type 2 diabetes mellitus (T2D), many of which encode proteins not previously suspected to be involved in the pathogenesis of T2D.Most GWAS for T2D have focused on populations of European descent, and GWAS conducted in other populations with different ancestry offer a unique opportunity to study the genetic architecture of T2D.We performed genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945 controls), 2,034 Malays (794 cases, 1,240 controls), and 2,146 Asian Indians (977 cases, 1,169 controls).In addition to the search for novel variants implicated in T2D, these multi-ethnic cohorts serve to assess the transferability and relevance of the previous findings from European descent populations in the three major ethnic populations of Asia, comprising half of the world's population.Of the SNPs associated with T2D in previous GWAS, only variants at CDKAL1 and HHEX/IDE/KIF11 showed the strongest association with T2D in the meta-analysis including all three ethnic groups.However, consistent direction of effect was observed for many of the other SNPs in our study and in those carried out in European populations.Close examination of the associations at both the CDKAL1 and HHEX/IDE/KIF11 loci provided some evidence of locus and allelic heterogeneity in relation to the associations with T2D.We also detected variation in linkage disequilibrium between populations for most of these loci that have been previously identified.These factors, combined with limited statistical power, may contribute to the failure to detect associations across populations of diverse ethnicity.These findings highlight the value of surveying across diverse racial/ethnic groups towards the fine-mapping efforts for the casual variants and also of the search for variants, which may be population-specific.",
"The T1DGC, using the same samples as in the MHC and candidate gene investigations, reevaluated 382 SNPs from 21 recently reported candidate genes, assembling nearly 4,000 ASP families and fully characterizing (through tagging SNPs and reported variants) the genetic contributions to type 1 diabetes risk.These results suggest that, aside from the MHC, 11p15 (INS), 2q33 (CTLA and other genes), 10p15.1 (IL2RA), and 1p13 (PTPN22), few of these published candidate genes can be replicated.In addition, a total of 1,715 SNPs were selected from the Wellcome Trust Case Control Consortium (WTCCC) GWA study of type 1 diabetes, and 581 SNPs were selected that exhibited association with autoimmune disease and type 2 diabetes loci (45,46).These studies confirmed established loci (above) (47,48) and suggested additional risk conferred by loci on chromosomes 5q31 (TCF7 [P19T], transcription factor 7, T-cell specific, HMG-box), 18q12 (FHOD3, formin homology two domain containing 3), and Xp22 (TLR8/ TLR7 toll-like receptor 8/toll-like receptor 7).Type 1 diabetes has many susceptibility loci and therefore pathways in common with autoimmune diseases.With the recent exception of GLIS3 (49), no genetic overlap was found between type 1 diabetes and type 2 diabetes loci (45,46,50).The dataset established by the T1DGC from its Candidate Gene Workshops is available from the NIDDK Central Repository.Genome-wide linkage.A number of genome-wide scans for linkage to type 1 diabetes have been reported (4,(51)(52)(53)(54)(55).All these studies consistently demonstrated linkage of type 1 diabetes to the MHC and specifically to the HLA genes on human chromosome 6p21.3.Additional regions with evidence of linkage have been identified, but many of these regions have not been reproduced in independent studies.",
"The latest and largest meta-analyses for T1D [4] and T1D diagnosis age [9] have been performed with variants from the ImmunoChip, a large scale but targeted genotyping platform which covers only loci previously associated with immunological diseases.We now took a genome-wide approach by performing a large genome-wide association study (GWAS) meta-analysis in 12,539 individuals with T1D from the Finnish Diabetic Nephropathy (Finn-Diane) Study, the UK Genetic Resource Investigating Diabetes (UK GRID), and Sardinia cohorts.Our aim was to identify variants affecting T1D diagnosis age and thereafter, utilizing the genome-wide coverage of our analysis, we aimed to link the variants to open chromatin indicating active gene expression in different cell types and finally, we performed transcriptome-wide association analyses in disease-relevant tissues.",
"Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits.However, the pathogenic mechanisms of most of these SNPs remain elusive.Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D.We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps.Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers.High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATACseq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL.Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions.Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints.Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions.Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.",
"Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge.Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs.We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls.We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk.This brings the number of T2D loci now confidently identified to at least 10.",
"A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants Laura J. Scott, 1 Karen L. Mohlke, 2 Lori L. Bonnycastle, 3 Cristen J. Willer, 1 Yun Li, 1 William L. Duren, 1 Michael R. Erdos, 3 Heather M. Stringham, 1 Peter S. Chines, 3 Anne U. Jackson, 1 Ludmila Prokunina-Olsson, 3 Chia-Jen Ding, 1 Amy J. Swift, 3 Narisu Narisu, 3 Tianle Hu, 1 Randall Pruim, 4 Rui Xiao, 1 Xiao-Yi Li, 1 Karen N. Conneely, 1 Nancy L. Riebow, 3 Andrew G. Sprau, 3 Maurine Tong, 3 Peggy P. White, 1 Kurt N. Hetrick, 5 Michael W. Barnhart, 5 Craig W. Bark, 5 Janet L. Goldstein, 5 Lee Watkins, 5 Fang Xiang, 1 Jouko Saramies, 6 Thomas A. Buchanan, 7 Richard M. Watanabe, 8,9 Timo T. Valle, 10 Leena Kinnunen, 10,11 Gonalo R. Abecasis, 1 Elizabeth W. Pugh, 5 Kimberly F. Doheny, 5 Richard N. Bergman, 9 Jaakko Tuomilehto, 10,11,12 Francis S. Collins, 3 * Michael Boehnke 1 * Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge.Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs.We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls.We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk.This brings the number of T2D loci now confidently identified to at least 10.",
"A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants Laura J. Scott, 1 Karen L. Mohlke, 2 Lori L. Bonnycastle, 3 Cristen J. Willer, 1 Yun Li, 1 William L. Duren, 1 Michael R. Erdos, 3 Heather M. Stringham, 1 Peter S. Chines, 3 Anne U. Jackson, 1 Ludmila Prokunina-Olsson, 3 Chia-Jen Ding, 1 Amy J. Swift, 3 Narisu Narisu, 3 Tianle Hu, 1 Randall Pruim, 4 Rui Xiao, 1 Xiao-Yi Li, 1 Karen N. Conneely, 1 Nancy L. Riebow, 3 Andrew G. Sprau, 3 Maurine Tong, 3 Peggy P. White, 1 Kurt N. Hetrick, 5 Michael W. Barnhart, 5 Craig W. Bark, 5 Janet L. Goldstein, 5 Lee Watkins, 5 Fang Xiang, 1 Jouko Saramies, 6 Thomas A. Buchanan, 7 Richard M. Watanabe, 8,9 Timo T. Valle, 10 Leena Kinnunen, 10,11 Gonalo R. Abecasis, 1 Elizabeth W. Pugh, 5 Kimberly F. Doheny, 5 Richard N. Bergman, 9 Jaakko Tuomilehto, 10,11,12 Francis S. Collins, 3 * Michael Boehnke 1 * Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge.Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs.We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls.We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk.This brings the number of T2D loci now confidently identified to at least 10.",
"GWAS-Identified Variants in Protein-Coding RegionsGWAS-identified variants associated with T2D risk include single nucleotide polymorphisms (SNP), deletions, insertions and short sequence repeats (6,92).Although the majority of the variants reside in intergenic or intragenic regions, a few (less than 5%) are in protein-coding regions.As potential drug targets, these variant-containing genes have been subjected to investigation in b cells in recent years (5) using cellular and mouse knockout systems, as described in the examples below:",
"A systematic search for the variants associatedwith Type 2 diabetes mellitus, a common complex disease was recently done317318N. Shahby testing 392,935 single-nucleotide polymorphisms in a French casecontrol cohort (13). They used Illumina Infinium Human1 BeadArrays, whichassay 109,365 SNPs chosen using a gene-centred design; and Human Hap300BeadArrays, which assay 317,503 SNPs chosen to tag haplotype blocks identified by the Phase I HapMap. There were 59 SNPs, showing significant association with the disease in genome-wide study, which were tested on a largercohort using the Sequenom iPlex assay.They identified four SNPs containingvariants that confer type 2 diabetes risk. These loci include a nonsynonymouspolymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing -cells, and two linkage disequilibrium blocksthat contain genes potentially involved in -cell development or function(IDEKIF11HHEX and EXT2ALX4). Even when genome-wide studies are possible, there are statistical difficulties arising due to multiple hypotheses testing. A good review of this issue andpossible solutions are presented in (14). 3.2.3. Pool-Based Genome-Wide Association StudiesGenotyping of individual samples for genome-wide association (GWA) studies may be cost-prohibitive.",
"Association of genetic variants in genes encoding T2D and obesity drug targetsThe study design consisted of initial discovery of variants with suggestive associations to targeted genotyping and in silico follow-up analyses (Fig. 1).We investigated the association of 121 variants in six genes encoding therapeutic targets in use or in development for T2D or obesity (CNR2, DPP4, GLP1R, SLC5A1, HTR2C, and MCHR1)-drawn from a recent targeted exome sequencing study of 202 genes encoding drug targets (8)-with variation in the following traits: T2D, obesity, body mass index (BMI), waist circumference, fasting glucose, fasting insulin, and 2-hour glucose (Fig. 1).In the \"discovery analysis,\" we identified seven variants potentially associated with T2D-or obesity-related traits (where P < 0.001 or which were in a target of interest to GSK and P < 0.05) (Table 1).For these seven variants, \"follow-up analysis\" was performed by targeted genotyping in up to 39,979 additional individuals of European ancestry.Where possible, in silico follow-up analysis was performed for traits and variants available in large-scale genetic consortia data."
],
[
"Elucidate the pathogenesis linking obesity and type 2 diabetesA 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.",
"The framework described in this paper is aimed to address two key questions: (1) Can biological processes be identified that are consistently deregulated in different models of insulin resistance and diabetes and that may be manifested in a tissue-dependent or independent manner? (2) On a higher level, can tissue or condition-specific interaction networks be identified that more precisely characterize different insulinresistance models and suggest causal mechanisms?Author SummaryType 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).",
"of Biochemistry, Biostatistics & Medical Informatics, University ofWisconsin, Madison, WI; Rosetta Inpharmatics, Seattle, WA; KineMed,Emeryville, CA; Dept Nutritional Sciences & Toxicology, University ofCalifornia, Berkeley, CA, USAInsulin resistance is necessary but not sufficient for the development of type 2diabetes. Diabetes results when pancreatic -cells fail to compensate for insulinresistance by increasing insulin production through an expansion of -cell massor increased insulin secretion. Communication between insulin target tissues and-cells may initiate this compensatory response. Correlated changes in geneexpression between tissues can provide evidence for such intercellularcommunication.",
"The origin of chronic inflammatory processes observed in metabolic disorders is still a matter of debate. 9The recent obesity epidemic is a driving force for the worldwide increasing incidence of type 2 diabetes (T2D) as more than 80% of patients with T2D are overweight.Obesity-induced insulin resistance is the dominant underlying pathophysiological factor. 10As insulin resistance and metabolic inflammation are frequently observed in parallel, research in the past decade has tried to connect these two phenomena.It is widely accepted that the aetiology of insulin resistance is complex and involves various pathways. 11It is, however, also increasingly established that inflammatory pathways are critically involved in the evolution of insulin resistance. 12Overnutrition and certain diets could represent major starting points as they might alter the gut microbiota, lead to changes in lipid metabolism, hepatic steatosis and finally systemic inflammation. 13 14It remains, however, unclear at which sites inflammatory processes are initiated and the GI tract with its significantly altered microbiota could reflect one of the early events in these disorders.",
"Type 2 diabetes mellitus (T2D) is a common complex disease whose pathogenic mechanisms are known to a considerable extent [8,9].Several organs including pancreatic islets, liver, skeletal muscle, adipose tissues, gut, hypothalamus and the immune system play a role in its pathogenesis [10].Numerous multifactorial mechanisms that include genetic and environmental factors related to obesity are involved in the development of insulin resistance and impaired insulin secretion [8,9].Insulin resistance is associated with inactivity, obesity and ageing [8].The insulin secreting pancreatic islet b cells respond to insulin resistance by enhancing their mass and metabolic function.T2D however develops when increase in insulin secretion by b cells is not able to keep pace with the increase in insulin resistance [8,11].The latter thus characterizes both prediabetic condition and T2D.Prediabetic insulin resistance state however does not always lead to diabetes; enhanced secretion of insulin by b cells compensates for deficient insulin action in a considerable proportion of prediabetic individuals who do not develop T2D.Though the inability of b cells to secrete enough insulin primarily typifies T2D, the dysfunction can also be demonstrated in normoglycemic subjects [12].Therefore, derangements in both insulin secretion and Figure 1.Schematic representation of the workflow.T2D GWAS genes do not directly relate (indicated by 'X' on the left side) to pathways associated with disease pathophysiology.Conspicuously, effect of identified risk variants on continuous glycemic measures in nondiabetic subjects chiefly explains only perturbation of insulin secretion, not insulin resistance.Further, the genes found as associated with the disease do not clearly relate to processes and pathways consistent with the known aspects of T2D pathophysiology.The main aim of the present study was to ask the question (indicated by '?' on the right side) if GWAS data when considered in conjunction with interactome, toxicogenome and disease transcriptome data reveal genome to phenome correlation in T2D.Data available in public domain for GWAS, interactome and toxicogenome was used in the analysis.For disease transcriptome, new experimental data was generated.We specifically examined if interaction network of genes reported in T2D GWAS, genes showing altered expression after treatment with various antidiabetic drugs, and genes that are differentially expressed in insulin responsive tissues in male and female T2D patients do converge on insulin secretion, insulin resistance and other T2D associated pathophysiological pathways.doi:10.1371/journal.pone.0053522.g001",
"This underlying -cell decompensation manifests clinically as elevated fasting andPREVpostprandial blood glucose levels, diagnostic criteria for diabetes [4,5]. In humans, diabetes is often correlated with obesity, leading to a long-standinghypothesis that insulin resistance is a consequence of overnutrition and elevated dietaryfatty acids [6]. Chronic metabolic overload has a detrimental effect on whole bodymetabolism, and there is increasing evidence that the liver and adipose play a causalrole to drive this metabolic disequilibrium (Figure 1).",
"Increasing evidence from more recent studies also suggested that infl ammatory processes may have a pivotal role in metabolic diseases: prospective studies have shown that high plasma interleukin 6 (IL -6) levels increased T2DM risk [116] , but confl icting associations were found between a promoter polymorphism (G -174C) in IL6 and T2DM [117,118] .In a large joint analysis of 21 case -control studies, representing > 20 000 participants in one of the largest association studies addressing the role of a candidate gene in T2DM susceptibility, the IL6 promoter variant was found to be associated with a lower risk (OR 0.91, P = 0.037) [119] .In addition, association between T2DM and IL6R -D358A was reported in Danish white people [120] , and with TNF G -308A promoter SNP in the Finnish Diabetes Prevention Study [118] .The effects of both IL6 and IL6R variants on developing T2DM risk in interaction with age have been reported in a prospective study of a general French population [46] .",
"In the long term, these new approaches should identify additional genes and metabolic markers; profi les obtained through these assessments could provide the level of detail needed to establish the mediator (or mediators) of the feedback loop that interconnects cells with insulin-sensitive tissues, and help to unravel the heterogeneity of the disease.Furthermore, these assessments should complement and advance present understanding of the best approaches to treat the dysregulated metabolic milieu in type 2 diabetes, which includes not only glucose but also fatty acids and aminoacids.Glucose 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. The epidemic of type 2 diabetesThe worldwide explosion of obesity has resulted in an ever-increasing prevalence of type 2 diabetes-a noncommunicable disease that aff ects more than 370 million people. 1 Without concerted eff orts to address the pathogenesis and treatment of this syndrome, the harmful macrovascular and microvascular outcomes of type 2 diabetes will remain a major burden for decades to come.In this Review we examine aspects of the pathogenesis and treatment of type 2 diabetes, and discuss future needs if the most damaging result of obesity is to be reversed.Glucose 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.",
"MetabolomicsA Metabolomics approach has been applied to diabetes in several population-based studies in recent years, summarized in [68].Metabolomics profiling was previously performed typically in a small subset of large populations, and the number of metabolites was limited.In recent studies MR analysis has been combined in metabolomics in order to claim causality of the metabolites found to be associated with the risk of diabetes.Nowak and collaborators investigated the effects of insulin resistance and insulin secretion on fatty acid levels [69].The original cohort included 910 elderly men (ULSAM cohort).Insulin sensitivity was determined with gold standard measurement, the hyperinsulinemic euglycemic clamp, and beta-cell function with a Disposition Index during an oral glucose tolerance test.A total of 192 metabolites were measured using untargeted plasma metabolomics by liquid chromatography/mass spectrometry.MR analysis was based on two separate cohorts (PIVUS and TwinGene, n 2,613) followed by replication in three independent studies profiled on different metabolomics platforms (KORA/TwinsUK, n 7,824; CHARGE consortium, n 8,961; and Finnish consortium, n 8,330).In the observational part of the study the authors reported that bile acid, glycerophospholipid and caffeine metabolism were associated with insulin resistance, and fatty acids biosynthesis markers with impaired insulin secretion.In MR analysis the authors discovered and replicated causal effects of insulin resistance on lower levels of monosaturated fatty acids, palmitoleic acid and oleic acid.Beta-cell function did not have causal effects on any metabolites measured.The limitation of this study is a relatively small size of the ULSAM cohort, and the limited number of metabolites measured.",
"Our understanding of the pathophysiology of T2DM has been aided by the discovery of novel disease biomarkers.High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6) and tumour necrosis factor (TNF), are associated with an increased risk of T2DM 30 , whereas a high concentration of adiponectin, which has anti-inflammatory effects, is associated with a reduced risk 31 .Lower levels of sex hormone-binding globulin are associated with increased risk 32 , as are higher blood concentrations of branched-chain and aromatic amino acids 33 .Gut flora metabolites might predict future risk of T2DM because the gut microbiota is involved in energy extraction from the diet, modification of host gene expression, and increasing metabolic endotoxaemia (the level of e ndotoxins in blood) and chronic inflammation 34 .",
"Several lines of evidence suggest that T2D is an inflammatory disease (Donath and Shoelson 2011).Recent results from clinical trials with anti-inflammatory drugs have supported this hypothesis, and immunomodulatory strategies for the treatment of T2D to lower blood glucose levels in patients have been proposed (Barry et al. 2016).Cellular oxidative stress is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D (Evans et al. 2003) by inducing an inflammatory response.",
"In this mini-review, we discuss this question in the context of recent advances in the understanding of the physiology of glucose metabolism in order to determine whether the classical under-standing of T2DM pathophysiology should be revised and more focus placed on the b-cell in the development of therapies for T2DM.In particular, we consider the extent to which the difficulty in identifying insulin resistance genes to date reflects limitations of study design, inadequate physiological assessment of insulin resistance or the complex underlying pathophysiology of insulin resistance (i.e.multiple parallel compensatory pathways).ConclusionWe 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.",
"| INTRODUCTIONChronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance, and -cell dysfunction in type 2 diabetes mellitus (T2DM) (Ehses, Perren, Eppler, Ribaux, & Pospisilik, 2007;Pickup, 2004).Recent studies have reported that the infiltration of the macrophages to pancreatic islets accelerates the -cell dysfunction.These macrophages secrete chemokines and stimulate the immune cell migration, as well as release of pro-inflammatory cytokines.In addition, the elevated glucose and palmitate concentrations increase chemokines release that induce granulocyte colony-stimulating factor and macrophage inflammatory protein-1 from human and mouse pancreatic islets both in vitro and in vivo (Ehses et al., 2007;Inoue et al., 2018).",
"To date, systematic review of the effects of disease risk variants on processes contributing to the diabetic state has mostly been restricted to the examination of basal indices of b-cell (BC) function or insulin sensitivity (2,3).These studies have demonstrated that most, but not all, of these loci exert their primary effects on disease risk through deficient insulin secretion rather than insulin resistance (IR) (2,(4)(5)(6).",
"The role for pro-inflammatory cytokines in regulating insulin action and glucose homeostasis and their function in T2DM has been suggested by several lines of evidence.Obesity, T2DM, and inflammation: Molecular mechanism(s) of associationIn obese people, insulin resistance is linked to the increased release of adipocyte-derived bioactive metabolites (ADBMs) such as lipids, free fatty acids, monocyte chemoattractant protein-1 (MCP-1), and pro-inflammatory cytokines. 30It should be emphasized, however, that although obesity is viewed as a predisposing factor to insulin resistance, other factors may also contribute.A study of young, insulin-resistant, lean offspring of patients with T2DM and insulin-sensitive controls of similar body mass index (BMI) showed similar plasma concentrations of TNF-, IL-6, and adiponectin between the insulin-resistant and insulin-sensitive groups. 34his suggests that in lean people, systemic inflammation may not play a significant role in the development of insulin resistance.In this case, proposed mechanisms for insulin resistance might then be attributed to a dysregulation of intramyocellular fatty acid metabolism. 14In the liver this would also include an altered expression of transcription factor 6- (ATF6) which controls expression of gluconeogenic genes. 35enetic predisposition also may contribute to the development of T2DM.Genome-wide association (GWA) and candidate gene studies over the past few years have so far uncovered 19 genes associated with T2DM. 36The disease-related genetic variants identified have high frequencies in the populations assessed although their individual contributions to increases in risk of T2DM are modest.Ongoing GWAs that target lowfrequency genetic variants and assess copy number variants (CNVs) in addition to single nucleotide polymorphisms (SNPs) are likely to identify additional loci associated with T2DM risk, and some of these may play a significant role in the risk of disease development. 36In lean subjects with T2DM, the dysregulation of fatty acid metabolism, the abnormal expression of gluconeogenic genes and the genetic predisposition necessitate the development of an additional set of biomarkers that target this subpopulation and relate to these risk factors."
],
[
"Key points Genome-wide association studies (GWAS) have identified >400 signals associated with the risk of type 2 diabetes mellitus (T2DM). The pancreatic islet has been identified as a key tissue involved in mediating GWAS signals in T2DM risk. Integrating genetic, epigenomic and cellular data can unlock the biology behind GWAS signals.",
"DISCUSSIONGenome-wide linkage scans aimed at identifying QTLs for type 2 diabetes and its associated traits are accumulating.However, findings seldom replicate across studies.Because type 2 diabetes represents a complex disorder with substantial clinical and genetic heterogeneity, efforts to define and identify genetically homogeneous subsamples",
"DiscussionThe present study applied a high-throughput functional genomics approach to identify the associations between genetic factors and inflammatory phenotype in patients with T1D.The results confirm a correlation between baseline immune-cell populations and ex vivo cytokine production in response to bacterial, fungal, non-microbial, and TLR ligand stimulations.We provide evidence for a direct link between T1D GWAS loci and immune functionality, particularly through circulating T cell subpopulations.We show that T cell alteration is largely driven by T1D genetics, while B cells do not show a significant association with T1D GWAS loci.The association between the proportion of CCR5+ Tregs and T1D susceptibility through CCR genes suggests that T1D-associated genetic variants contribute to alteration of immune function through a cumulative effect.Finally, out of 28 genome-wide significant genetic loci regulating immune-cell proportions and cytokine production, we identified 12 immune phenotype QTLs specific to 300DM.We also found 11 druggable genes as candidates for therapeutic intervention.Altogether, this study provides several novel insights into the genetic variability of immune traits in T1D.In the present study we aimed to comprehensively describe the immunopathological consequences of the genetic variants linked to T1D susceptibility, using a high-throughput functional genomics approach.As a part of the Human Functional Genomics Project (HFGP) (Netea et al., 2016), we carried out deep immunophenotyping in peripheral blood samples from a cohort of 243 T1D patients (300DM) using cell subpopulation composition and cytokine production upon stimulations as proxies of immunological function.Part of the results were then compared to those obtained in a populationbased cohort of 500 healthy individuals (500FG) that successfully characterized the impact of genetic factors (Aguirre-Gamboa et al., 2016;Li et al., 2016) on immune responses in healthy individuals.Here, we systematically evaluate the genetic regulation of the immune phenotypes in T1D and show how genetic variations affect immune-cell traits and cytokine production in response to stimulations.In total, we identify 15 genome-wide significant genomic loci (p-value < 5 10 -8 ) associated with immune phenotypes in the 300DM cohort, including 12 novel loci that have never been reported in any healthy population study.These data provide a deeper understanding of the immune mechanisms involved in the pathophysiology of T1D and affecting the general inflammatory response and may open avenues toward the development of novel diagnostics and potentially immunotherapies.",
"These GWA studies, as well as detecting new loci, provided the first 'genome-wide' perspective of the landscape of T2D susceptibility and thereby enabled clearer 'bench-marking' of other claimed T2D-susceptibility effects for which the accumulated evidence from candidate-gene studies remained somewhat equivocal [40].Examples include variants in the genes encoding calpain-10 (CAPN10; thought to be involved in b-cell function), insulin (INS; an obvious candidate) and PC-1 (ENPP1; the product of which is known to modulate insulin-receptor function).None of these genes has featured prominently in GWA analyses to date and, although this does not necessarily exclude a contribution to T2D predisposition, it indicates that the main effects attributable to these variants are small and/or subject to substantial modification by genetic background or environmental exposures.Either way, it seems likely that exhorbitantly large sample sets will be required before such signals can attain the standard of proof now available for the loci described in Table 1.",
"Genome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).",
"IntroductionGenome-wide association studies (GWAS) have identified approximately 80 loci robustly associated with predisposition to type 2 diabetes (T2D) [1][2][3] and a further 70 influencing a range of continuous glycemic traits [4][5][6][7][8][9][10] in non-diabetic subjects.There is substantial, though far from complete, overlap between these two sets of loci.Physiological studies in non-diabetic individuals indicate that most of these loci primarily influence insulin secretion rather than insulin sensitivity, highlighting a key role for the pancreatic islets of Langerhans in the mechanistic underpinnings of these association signals [11,12].These findings have motivated efforts to catalogue the epigenomic and transcriptional landscape of human islets and to apply these findings to deliver biological insights into disease pathogenesis.Recently, it has been shown, for example, that GWAS signals for T2D and fasting glucose show significant co-localization with islet enhancers [13,14].",
"It has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.",
"Functional pathway and network analyses of GWAS data combined with proteomic/transcriptome data, i.e. expression data, have also highlighted how candidate genes interact and may be involved in immune-related mechanisms (6)(7)(8).This has added significantly to our understanding of T1DM etiology.Finally, T1DM susceptibility variants may affect both development Pociot et al. (9) and persistence (10)(11)(12) of autoimmunity and thus might serve as potential intervention targets in clinical studies aiming at diminishing autoimmunity.ConclusionsA major challenge is to translate GWAS findings into causal variants and target genes.The Immunochip effort has greatly contributed to our understanding of disease mechanisms by identifying pathways, which could not be linked to diabetes by existing hypothetical models.Diabetes is probably a much more diverse disease than the current subdivision into T1DM and T2D implies and a more precise subdivision into subgroups may also pave the way for a more individualized medicine.A holistic systems biology approach will also be required to obtain a complete picture of how genetic variation alters a protein function leading to diabetes.The rapid technology development during the past years holds promises that this will be possible in a not too distant future.",
"IntroductionGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4].",
"Genome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.Genome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.",
"More recently, GWA studies have become feasible in large cohorts of patients and controls.Using this approach compelling evidence for genetic variants involved in type 1 diabetes [31][32][33], type 2 diabetes [31,[34][35][36][37], age-related macular degeneration [38], inflammatory bowel disease [39], heart disease [40,41] and breast cancer [42] have already been described.",
"Molecular Biology Reports, 37: 501505. Lyssenko V, Groop L (2009) Genome-wide association study for type 2 diabetes: clinical applications. Current Opinion in Lipidology, 20: 8791. Maltecca C, Weigel KA, Khatib H, Cowan M, Bagnato A (2009) Whole-genome scan for quantitative trait loci associated with birth weight, gestation length and passive immune transfer in aHolstein Jersey crossbred population. AnimalGenetics, 40: 2734. Mardis ER (2008a) The impact of next-generationsequencing technology on genetics. Trends in Genetics, 24: 133141. Mardis ER (2008b) Next-generation DNA sequencing methods. Annual Review of Genomics and Human Genetics, 9: 387402.",
"How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner.",
"Background: Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D).However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes.There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology.",
"INTRODUCTIONMultiple genome-wide association studies (GWASs) have correlated type 2 diabetes mellitus (T2DM) with genetic variants, yielding a large number of loci and associated gene products that are linked to the disease phenotype-often with little or no insight into the mechanism underlying that link (Hivert et al., 2014).The current challenge is to establish robust systems to systematically evaluate the role of these loci using disease-relevant cells.Previous studies have used patient samples, cell lines, or animal models to seek mechanistic insight but with significant limitations.Large variation is observed in primary patient samples, perhaps due to genetic heterogeneity, whereas animal models present major physiological and metabolic differences that hamper understanding of the precise function of human genes in T2DM.Therefore, a robust system to systematically evaluate the role of T2DM-associated genes using disease-relevant human cells will provide an important tool for diabetes research and spur the development of precision (allele-specific) therapies, exemplified by the use of sulfonylurea drugs to treat patients carrying certain KCNJ11 mutations (Gloyn et al., 2004).",
"Background: Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D).One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes.However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.Methods: Here, we describe implementation of an analytical pipeline to address this question.First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals.Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks.Third, we use GWAS data to test the T2D association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network.",
"Genetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?"
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