{
  "question": [
    "How is gene expression in the liver affected by diabetes?",
    "Is any of the genes SH2B3, IFIH1 or ERBB3  related to diabetes?",
    "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets",
    "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets",
    "Is the gene TCF7L2 involved in diabetes?"
  ],
  "answer": [
    "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.",
    "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.",
    "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.",
    "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.",
    "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D)."
  ],
  "contexts": [
    [
      "Studies have reported that SLC2A9 is expressed in both kidney and liver of human and mice and is upregulated in diabetes mice 25 .The SLC2A9 expression was found to be governed by p53 gene and is mediated by oxidative stress 26 .Oxidative stress play major and deterministic role in patho-physiology of T2DM and has been observed to be higher in T2DM patients than healthy controls 27 .The higher expression of SLC2A9 in diabetic condition may be governed by higher oxidative stress in diabetics.In a recent study, Hurba et al. observed that there is no significant difference in transport activity of coding rs16890979 (Val253Ile) variant containing protein and wild type protein in Xenopus oocyte expression system 28 .The higher activity of SLC2A9 in T2DM subjects compared to normoglycemics may be attributed to higher expression of total SLC2A9 protein in T2DM condition.",
      "Multiple studies on the transcriptome level have been performed that emphasize the diversity of the disease and the complex pathophysiological interactions between different tissues, including fat, muscle, liver, pancreatic beta cells and brain [1].In several human studies, tissue biopsies from diabetic and normoglycaemic individuals have been profiled [12,13].In mouse studies differences in diet or mouse strains have been used to identify distinct expression profiles [14][15][16].Complementary ChIP-on-Chip studies reveal the associated gene regulatory network of important transcription factors (TFs) active in the rele-vant tissues [17,18].In the context of the onset of diabetes, several studies on the proteomic level have revealed differential expression of intracellular proteins as well as of secretory proteins in adipose tissue [19].Despite the availability of these large amounts of data, their common content as well as their specific differences, in particular in gene sets between human and rodent studies, has not yet been systematically evaluated.On the other side Slc2a2 is also changed in liver.Ptpn1 is expressed in all tissues showing only small fold-changes.Several genes from OMIM or KO-mice do not change at all on the expression level.This indicates that only the complete loss of the associated protein alters the system whereas the gene's expression is not altered in T2DM.For KO-mice we also see a strong tendency to genes only expressed in mice.",
      "The activities of several key gluconeogenic enzymes are increased in both young and adult diabetes mice as compared with controls [4,7] in spite of the higher than normal circulating levels of plasma insulin.In contrast the activities of the insulin dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme and acetyl-CoA synthetase are increased indicating a normal response to elevated concentrations of plasma insulin [7].As in the obese mouse, insulin resistance coupled with a disappearance of receptor sites has been a consistent finding in most tissues studied [26].",
      "Regulation of GWAS diabetes genes by glucose in pancreatic isletsMany of the recently discovered type 2 diabetes genes have been suggested to affect the development and/or function of pancreatic islets [6].The function, growth and survival of -cells can be regulated acutely and chronically by glucose [34].Thus, we examined whether the new type 2 diabetes susceptibility genes are regulated by overnight incubation in low (5 mM) or high (25 mM) glucose (Figure 5).Most genes were significantly or tended to be downregulated under conditions of high glucose.Cdkal1, Cdkn2a (Arf, P = 0.07), Ide, Jazf1, Camk1d, and Tspan8 (P = 0.06) expression levels were decreased ~50-60%.Meanwhile, the expression of Cdkn2b, Hhex (P = 0.10), Cdc123, Adamts9 (P = 0.09), and Thada were reduced 30-40%.To ensure the islets incubated in high glucose did not have globally decreased expression, we examined the expression of Txnip, which has been shown to be highly upregulated by glucose [35] and found that its expression was still significantly elevated in the islets cultured in high glucose (Figure 5).Mouse islets consist of -cells and other cell types.Thus, the MIN6 -cell line was also examined.We found that all the genes were expressed in this cell line (not shown), although this does not preclude that they also are expressed in other cell types within the islet.Figure 5 Regulation of new diabetes genes by glucose levels in pancreatic islets.Data are shown as fold-change, (2 Ct )  2 CtSE[87], relative to those observed in the islets incubated in low (5 mM) glucose.Each group is the average of three replicates, each of which was comprised of pooled islets from two mice. * P < 0.05, *** P < 0.001.It has been hypothesized that most of the new genetic variants affect -cell function, development or survival but not insulin sensitivity [6].Consistent with this, we found all of the genes except Adam30 and Cdkn2a were expressed in pancreatic islets.These genes were expressed, however in the transformed -cell line, MIN6.The expression of all the genes except Lgr5 decreased following incubation of the islets in high glucose concentrations.It can thus be hypothesized that these genes may normally play a beneficial role in islet function, and a reduction in the expression of these genes could contribute to glucotoxic -cell dysfunction or survival.However, we also found evidence that most of the genes could have potential roles in other metabolically-relevant tissues.Genes affecting insulin sensitivity may be expected to be expressed in peripheral insulin sensitive tissues, such as liver and adipose tissue, and be responsive to metabolic status.Consumption of a high fat diet was associated with a tendency for the expression of several of these genes to be decreased.Similarly, many of the genes were regulated by feeding and fasting.Only the two splice isoforms of Cdkn2a had no evidence of metabolic regulation in any of the other tissues examined.",
      "A recent study assessed gene expression in different islet cell types including the insulin-producing b-cells (Dorrell et al, 2011).A comparison showed that 240 of our 254 genes are covered by the microarray used by these authors.In all, 170 of these genes have a positive presence call in b-cells.This indicates that the majority of the genes we detected as differentially methylated in T2D islets are expressed in non-diabetic b-cells to a sufficient amount to be reliably detected by microarrays, that is, these are genes actively transcribed in b-cells.",
      "Figure3: Challenges with identifying gene expression alterations in type 2 diabetes.Gene expression measurements from RNA-seq data typically represent only a snapshot of tissues' or cell types' transcriptome at a given point in time.In recent comparative analyses of islet intact and single cell transcriptomes from T2D and ND individuals, relatively few genes are significantly altered despite the clear phenotypic differences between them.This may suggest that the mechanisms that precede islet failure and T2D pathogenesis are post-transcriptional and cannot be detected in conventional RNA-seq analyses.However, it is also possible that the putative paths of these genes' alterations over the course of islet physiological decline and T2D development are simply being missed.Genes that are important for islet function and resilience (e.g., Gene A) and those whose expression directly induces or is the consequence of islet failure (e.g., Gene C) may be detected in a comparative analysis between islets at healthy and decompensated states.However, response genes that are temporarily induced by islet stress (e.g., Gene B) would not be detected in this comparison.",
      "Figure 2. Diabetes increases the variability of gene expression levels in other experimental paradigms. (A) Microarray data from gene expression profiling in placentas from normal compared to diabetic pregnancies (Salbaum and Kappen, unpublished data) were processed as shown in Figure1B: the coefficient of variation was determined for each gene probe, and a histogram was obtained after logarithmic transformation.The curve representing the diabetic placenta samples was shifted to higher values, similar to the results obtained in embryos from diabetic pregnancies. (B) Publicly available microarray data from diabetic versus normal human kidney (GEO record GSE1009) were treated in the same fashion as described for embryonic or placental gene expression data.Similar to our own datasets, the curve representing the coefficients of variation for the diabetic samples is shifted toward higher values, again implying that the variability of gene expression levels is higher in diabetic samples compared to control samples.Our analysis of various expression profiling data sets suggests that, in the respective paradigms (mouse embryo, mouse placenta, and human kidney), diabetes leads to an increase in the variability of gene expression, possibly by affecting the precision of gene regulation in general.Although this would be consistent with our model for maternal diabetes-elicited NTD etiology, it is important to note that the currently available gene-profiling surveys were never designed to capture variability of gene expression as an explicit experimental parameter.In fact, microarray experiments are typically structured to eliminate variability as a confounding element as much as possible, such as through the use of pooled samples.To directly measure the extent of variability of gene expression brought about by maternal diabetes, it would be necessary to conduct expression-profiling experiments with individual embryo samples, and with a higher number of samples for each side of the experimental paradigm.In this way, it would be possible to not only classify genes according to their change in expression, but also according to their change in variability of gene expression.Such experiments would define which genes exhibit increased variability in expression levels.According to our model, these would be candidate genes to trigger birth defect pathogenesis.Functional assays will then be required to test which genes of this ''highly variable'' group are able to interact with the ''susceptibility'' component-NTD genes with consistent change of expression in all exposed individuals.",
      "All these studies show that gene expression, in pancreatic islets, is very sensitive to nutrients and bioactive compounds present in food.The altered expression of genes involved in  cell nutrient sensing, insulin synthesis, cell cycle, survival/apoptosis and cell maintenance can impair  cell function and at the end facilitates  cell failure (Figure 2).Figure 2. Effects of nutrients on  cell gene expression.Pancreatic  cells are able to sense dietary nutrients and respond to them releasing insulin.Different nutrients and their metabolites affect transcription of genes very important for maintenance of  cell function and integrity.Flavonoids upregulate the expression of genes involved in insulin synthesis, nutrient-induced insulin release and  cell proliferation and downregulate genes implicated in  cell apoptosis.Proteins positively regulate insulin synthesis, insulin release,  cell proliferation and growth upregulating the expression of mTOR, calcineurin and Pdx1.Fats upregulate OXPHOS genes leading to the generation of metabolic coupling factors critical for insulin exocytosis.On the other hand, a chronic exposure of -cells to high levels of fats (mainly saturated fatty acids) induces excessive levels of ROS and pro-inflammatory cytokines, leading to an increased apoptosis.The upregulation of the expression of cytokine genes and genes involved in pro-inflammatory signaling pathways, together with the downregulation of genes implicated in the antioxidant defenses of  cells, contribute to  cell apoptosis.Moreover, chronic exposure to fats and their byproducts downregulate the expression of genes necessary for insulin synthesis, nutrient-induced insulin release,  cell integrity, maintenance and survival (Pdx1 and MafA).Impairment of -cell function is a hallmark of pancreatic -cell failure and may lead to development of DM.",
      "It is worth mentioning that in [132], a meta-analysis study was conducted, where a collection of gene expression datasets of pancreatic beta-cells, conditioned in an environment resembling T1D induced apoptosis, such as exposure to proinflammatory cytokines, in order to identify relevant and differentially expressed genes.The specific genes were then characterized according to their function and prior literature-based information to build temporal regulatory networks.Moreover, biological experiments were carried out revealing that inhibition of two of the most relevant genes (RIPK2 and ELF3), previously unknown in T1D literature, have a certain impact on apoptosis.",
      "The known tissue specificity of gene expression regulation means that the most informative studies will measure transcript levels in the specific tissue(s) relevant to the disease.In the case of type 2 diabetes, characterization of physiological responses (e.g., stimulus-induced insulin secretion, insulin sensitivity) suggests most loci are associated with defects in pancreatic b-cell function (2,3,7).Therefore there is a real need to measure gene expression in human b-cells (or whole islets, as these have been shown to be a suitable proxy [8]).There have, however, been very few reports linking type 2 diabetesassociated variation with islet gene expression using the classical eQTL approach (9,10).",
      "Young diabetic mice, at the stage whenthey still have an increased capacity to utilize glucose,had increased hepatic activities of glueokinase, citratelyase and acetyl-CoA synthetase (Table 3). However,glueose-6-phosphate dehydrogenaseactivity in the livers of micein early diabetic stages was notquite as great as in normal livers. This enzyme may be the most sensitive to the action of insulin of thefour enzymes mentioned since thelivers of some diabetic mice inthe group had glucose-6-phosphatedehydrogenase activity equal tothat from normal mice.Thus theoverall decrease in activity in liversfrom the group of 12 diabetic miceprobably includes data from a fewmice in the transitional stage whenthe ability to metabolize glucosewas rapidly declining. Activities of allfour enzymes in liver from older diabetic mice with blood sugar concentrations approaching 600 mg / 100 mlwere greatly reduced. Enzyme activities in adiposetissue showed the same generalpatterns as those in liver with the exception that glucose-6-phosphate dehydrogenase was clearly elevated inadipose tissue from the youngerdiabetic mice over that seen in adipose tissue from normal controls.Many of the liver cells of the diabetic mouse arehypertrophied and filled with fat droplets, especiallyin areas surrounding the hepatic veins (Fig. 5). Theincrease in glycogen content seen in Table 1 is notvisible histologically as PAS-positivc, diastase-digestible material, but a striking difference in glycogendistribution in livers from normals and from diabeticsis apparent. I n normal liver (Fig. 4), glycogen isdistributed fairly uniformly throughout, whereas int h a t from the diabetic (Fig.",
      "To evaluate the effects of hyperglycemia or other metabolic consequences of DM per se on expression, we identified 12 genes altered in DM as compared with both nondiabetic groups but not as a function of family history (Table 4, which is published as supporting information on the PNAS web site).This included a 70-kDa heat-shock protein (HSP701A), which was decreased by 42% in DM and whose expression correlated inversely with fasting glucose for all subjects (r  0.77).Expression of a related HSP70 gene was previously found to be reduced in Caucasian diabetic subjects (20).Genes differentially expressed between control and diabetic subjects may reflect either the pathophysiology of insulin resistance (primary alterations) or secondary effects of hyperglycemia, hyperlipidemia, and other metabolic factors.To identify potentially primary expression changes associated with insulin resistance, we compared gene expression in FH (nondiabetic but insulin resistant) and FH controls.One hundred sixty-six genes were differentially expressed between FH and FH (P  0.05) (Table 3, which is published as supporting information on the PNAS web site); 55 were common to both [FH vs. DM] and [FH vs. FH] comparisons.No single gene remained differentially expressed after Benjamini-Hochberg multiple comparison testing.However, ontology classification analysis (17) revealed that 20S and 26S proteasome complexes were the top-ranked cellular component terms (Z 7.7 and 7.3); mitochondrion-linked genes were also overrepresented (Z 3.2).Cell structure (P  0.004), protein degradation (P  3.7  10 4 ), and energy generation (P  0.003) groups were represented to a greater extent than expected for random distribution; with multiple comparison testing, the protein degradation26S proteasome (P  1  10 5 ) group remained significant."
    ],
    [
      "Figure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5  10 9 ).",
      "All the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency.",
      "One method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12).",
      "ResultsImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p  0.536), while DEA produced a comparably weak enrichment score (p  0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes.",
      "35ABSTRACT 11A GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETESESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLEREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITYMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,C Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, STurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD AttieDepts.",
      "Second, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion.",
      "In summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems.DISCUSSIONWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype.",
      "These observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 .",
      "A recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)- signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73).",
      "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated GenesFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients  and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used.",
      "In this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion.",
      "At present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients.",
      "In summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets.",
      "Results.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion.",
      "The authors then used mouse liver and adipose expressiondata from several mouse crosses to construct causal expression networks for the ERBB3 andRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with anyknown Type I diabetes genes whereas RPS26 is associated a network of several genes thatare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysisdemonstrates the power of combining human and mouse data with a network basedapproach that has been proposed for use in drug discovery (Schadt et al.",
      "In conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a  broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D.Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of 2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of 2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.",
      "Finally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]."
    ],
    [
      "Researchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.",
      "Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.",
      "In conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes.",
      "Genomics of T2DDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.",
      "It is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power.",
      "To date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.",
      "In recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management.",
      "Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,It is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8].The aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.Nutrient-or dietary pattern-gene interactions in the development of DM.",
      "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.",
      "In a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases.In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.",
      "The public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13).",
      "Genomics for Type 2 DiabetesMany research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's."
    ],
    [
      "Researchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.",
      "enetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes.",
      "Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.",
      "Diabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara-  metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes.",
      "In conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes.",
      "To date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.",
      "In recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management.",
      "Nonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug.",
      "DiscussionOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.",
      "Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,The aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.It is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8].",
      "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.",
      "In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.In a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases.",
      "Genetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1"
    ],
    [
      "In 2006, a large-scale association study identified TCF7L2 as an important genetic factor for T2D in Icelandic individuals [10].This discovery was a significant breakthrough as this association was then widely confirmed in populations of European origin and other ethnic groups, such as Japanese and American individuals [50][51][52][53][54][55][56][57].Therefore, TCF7L2 was regarded as the most significant T2D susceptibility gene identified to date.3.1.Impact of TCF7L2 on the Risk of T2D.TCF7L2 is the most intensively studied locus for T2D risk so far.The risk alleles of TCF7L2 were associated with enhanced expression of this gene in human islets as well as impaired insulin secretion both in vitro and in vivo.The authors also observed an impaired incretin effect in subjects carrying risk alleles of TCF7L2 and proposed the engagement of the enteroinsular axis in T2D [119].Dennis and colleagues then verified this result and indicated that TCF7L2 variant rs7903146 affected risk of T2D, at least in part, through modifying the effect of incretins on insulin secretion.This was not due to reduced secretion of glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1), which exhibit an important physiological role in boosting insulin secretion following meals, but rather due to the effect of TCF7L2 on the sensitivity of -cells to incretins [120].TCF7L2 has also been linked to altered pancreatic islet morphology as exemplified by increased individual islet size and altered alpha and beta cell ratio/distribution within human islets [121].This phenomenon is also observed in other in vivo or in vitro studies [122][123][124].This further strengthened the evidence for the role of TCF7L2-associated alteration of cell types in islets in the pathogenesis of T2D.TCF7L2 encodes the transcription factor TCF4 which is related to Wnt signaling pathway and which plays a critical role in the pathogenesis of T2D.The major effector of the canonical Wnt signaling pathway is known as catenin/TCF.This bipartite transcription factor is formed by free -catenin (-cat) and a member of the TCF protein family, including TCF7L2 (previously known as TCF-4) [125].GWAS have revealed the involvement of a Wnt ligand (Wnt-5b), Wnt coreceptor (LRP-5), and the Wnt pathway effector TCF7L2 in the development of diabetes [126].Several previous studies also provide evidence that the -catenin/TCF axis participates in pancreatic cell proliferation and differentiation [127][128][129][130][131]. Treatment of -cells with purified Wnt protein or activated -catenin augmented the proliferation of these cells [132].Intriguingly, deletion of -catenin within the pancreatic epithelium resulted in an almost complete lack of acinar cells, whereas deletion of -catenin specifically in differentiated acinar cells had no such effect [128], suggesting that the TCF7L2-related Wnt signaling mainly perturbs pancreatic growth but not pancreatic function.However, deletion of islet TCF7L2 expression from -cells did not show any demonstrable effects on glucose-stimulated insulin secretion (GSIS) in adult mice, whereas manipulating TCF7L2 levels in the liver caused hypoglycemia and reduced hepatic glucose production [133].In concordance with these results, risk alleles in TCF7L2 were associated with hepatic but not peripheral insulin resistance and enhanced rate of hepatic glucose production in human [119].Therefore, TCF7L2-related disruption of -cell function is probably the indirect consequence of primary events in liver or other organs/systems.",
      "Variant of transcription factor 7like 2(TCF7L2) gene confers risk of type 2 diabetes. Nat. Genet. 38: 320323. doi: 10.1038/ng1732GuhaThakurta D., Xie T., Anand M., Edwards S.W. , Li G., WangS.S. & Schadt E.E. 2006. Cis-regulatory variations: A study ofSNPs around genes showing cis-linkage in segregating mousepopulations. BMC Genomics 7: 235. doi: 10.1186/1471-21647-235Gunter C. 2008. Quantitative genetics. Nature 456: 719. doi:10.1038/456719aHaines J.L. , Hauser M.A. , Schnidt S., Scott W.K. , OlsonL.M. , Gallins P., Spencer K.L. , Kwan S.Y. , Noureddine M.,Gilbert J.R., Schnetz-Boutaud N., Agarwal A., Postel E.A.",
      "One obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4].Our data also lead us to conclude that TCF7L2 could also play a role in the pathogenesis of type 2 diabetes.Note that although TCF7L2 is known to have multiple isoforms, our expression data revealed no significant differences in these splice variants (ESM Table 6).",
      "In conclusion, our study confirms the involvement of TCF7L2 gene in the T2DM susceptibility.Moreover, as shown also by the logistic regression analysis results, we describe a significant contribution of the TCF7L2 genetic variability to the emerging diabetic complications such as retinopathy and CAN.DiscussionThis study examined the relationships between genetic variants of TCF7L2 gene and T2DM in an Italian population.Although the disease progression results from an interplay of environmental factors and genetic predisposition, in recent years TCF7L2 gene has been considered the strongest genetic determinant for the risk of developing T2DM [2-4, 19, 20].The gene encodes a transcription factor of the canonical Wnt signaling pathway, expressed in several tissues, known to have developmental roles in determining cell fate, survival, proliferation and movement [9].Wnt signaling plays an important role also in B-cell proliferation and insulin secretion and influences synthesis of glucagon-like peptide 1 (GLP-1) in intestinal L-cells [21].In our study, besides the confirmation of the role of TCF7L2 gene in the susceptibility to T2DM, we investigated whether variants of this gene could also be associated with diabetic complications in our diabetic population.",
      "Recently, two moderately linked intronic SNPs (rs7903146 and rs12255372; r 2  0.7) in the confirmed diabetes risk gene TCF7L2 [transcription factor 7-like 2 (T-cell-specific, HMG-box); OMIM entry no.602228] were shown to affect GLP-1 responsiveness of -cells, as evidenced by a hyperglycemic clamp combined with GLP-1 infusion (199).This was confirmed by comparison of the effect of the representative SNP rs7903146 on insulin secretion upon an oral vs. an iv glucose load (200).Plasma GLP-1 levels were not different between the genotypes (199,200).TCF7L2 encodes a component of the bipartite transcription factor complex -catenin/transcription factor 7-like 2 that is involved in the Wnt signaling pathway (236).Using knockdown by RNA interference and overexpression by transfection, it was demonstrated, in human and murine islets, that TCF7L2 is required for -cell survival and -cell proliferation as well as for glucose-and incretin-stimulated insulin secretion (237).Furthermore, expression of the insulin gene was found to strongly correlate with TCF7L2 expression (200) and was decreased after TCF7L2 knockdown, suggesting that the insulin gene represents a direct target gene of transcription factor 7-like 2 (238).Importantly, novel results of Maedler's group (239) revealed that the expression of GLP-1 and GIP receptors in human islets likewise depends on the presence of transcription factor 7-like 2 providing a plausible explanation for this gene's involvement in incretin responsiveness of -cells.",
      "In studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in -cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting -cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the -cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the -cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype.",
      "The first moves towards large-scale association mappingThe earliest indication that the 'hypothesis-free' association approach to gene identification might succeed for T2D came from the discovery that variants within the transcription factor 7-like 2 (TCF7L2) gene had a substantial effect on T2D susceptibility [15].TCF7L2 encodes a transcription factor that is active in the Wnt-signalling pathway and that had no 'track-record' as a candidate for T2D; indeed, this susceptibility effect was detected through a search for microsatellite associations across a large region of chromosome 10 that had been previously implicated in T2D susceptibility by linkage [16].Subsequent fine-mapping efforts localized the likely causal variant(s) to an intron within TCF7L2 [15,17].The fact that this signal was found within a region of apparent T2D linkage seems to have been serendipitous, because none of these variants within TCF7L2 are capable of explaining the linkage effect [15,17].Across a swathe of replication studies [3][4][5][6][7]18], it has become clear that TCF7L2 variants have a substantially stronger effect on T2D risk than those in PPARG and KCNJ11, with a per-allele odds ratio of $1.4 (Table 1; Figure 2).As a result, the 10% of Europeans that are homozygous for the risk allele have approximately twice the odds of developing T2D as those carrying no copies [15,18].The evidence implicating variants within TCF7L2 in T2D susceptibility has naturally prompted efforts to understand the mechanisms involved.Current evidence indicates that alteration of TCF7L2 expression or function disrupts pancreatic islet function, possibly through dysregulation of proglucagon gene expression,  LGR5, leucine-rich repeat-containing G-protein coupled; NOTCH2, Notch homologue 2 (Drosophila); PPARG, peroxisome proliferator-activated receptor gamma; SLC30A8, solute carrier family 30 (zinc transporter), member 8; TCF7L2, transcription factor 7 like 2; THADA, thyroid adenoma associated; TSPAN8, tetraspanin 8; WFS1, Wolfram syndrome1.b Estimates of effect size (given as per-allele odds ratios, i.e. the increase in odds of diabetes per copy of the risk allele) and risk-allele frequencies are all reported for Europeandescent populations based on available data (Figure 2).",
      "The genetic association between T2D and variants in transcription factor 7-like 2 (TCF7L2) was first discovered in a  2).It is interesting that the T allele of rs7903146 increases T2D risk while decreasing BMI, opposing the idea that increased BMI leads to insulin resistance and T2D.In comparison to FTO and MC4R variants, TCF7L2 variants have a much larger effect on T2D risk and a smaller effect on BMI, which might indicate that the TCF7L2 variants act via T2D to affect BMI (Fig. 2).TCF7L2 is a transcription factor functioning in WNT signaling, which is crucial for cell proliferation, motility, normal embryogenesis, and regulation of myogenesis and adipogenesis (reviewed in [96]).Although the causal variant is still unclear, the T2D risk allele appears to act via lowering the levels of insulin secretion and influencing beta-cell function (reviewed in [51,96,97]).",
      "To date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic  cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through  cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic  cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47].",
      "From the first GWA study of T2D, published recently in Nature [141], the strongest association observed was with a gene that was already established as having a role in the disease, namely the Wnt-signaling pathway member, transcription factor 7-like 2 (TCF7L2) [142], which has already been extensively independently replicated [143][144][145][146][147][148][149][150][151][152].This association has now been refined utilizing a West African patient cohort [153]; this is due to the fact that, in this cohort, the associated SNP is contained in a smaller LD block due to higher haplotype diversity in populations of African ancestry and thus the region most likely to contain the functional variant was narrowed down.The precise mechanism of action for this variant and its influence on the susceptibility to T2D is still to be elucidated; but it is speculated that it could operate through the alteration of levels of the insulinotropic hormone, GLP-1, one of the peptides encoded by the proglucagon gene whose expression in enteroendocrine cells is transcriptionally regulated by TCF7L2 [118].In tandem with insulin, GLP-1 has a strong influence on blood glucose homeostasis [118].Indeed, GLP-1 analogs and inhibitors of dipeptidyl peptidase IV are currently in clinical development.It has been noted that individuals with both impaired glucose tolerance and the at-risk TCF7L2 variant are more likely to go on to develop T2D, with the effect reported to be stronger in a placebo group than in metformin and lifestyle-intervention groups [143].The variant is also associated with decreased insulin secretion, but not increased insulin resistance at baseline [143].The risk-conferring genotypes in TCF7L2 are thus associated with impaired -cell function, but not with insulin resistance and may, therefore, give some indication on optimal therapeutic intervention for the one in five T2D cases this variant impacts.",
      "TCF7L2Transcription factor 7-like 2 was first implicated when a signal associated with Type 2 diabetes on chromosome 10q was shown in Icelandic populations to host a microsatellite DG10748, containing single nucleotide polymorphisms rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene [20], associated with a ~45% increase in Type 2 diabetes risk per allele.As such, the TCF7L2 locus presently represents the strongest known genetic determinant of Type 2 diabetes.Risk allele carriers show impaired insulin production [21] and b-cell dysfunction in vitro [22].",
      "Among all the loci, TCF7L2 so far has shown the strongest association with the largest effect size for type 2 diabetes in Europeans (5,(7)(8)(9)(10)(11)(12), Amish (25), and Indians (22,26,27), but not in Chinese (28) and Japanese (29) subjects.The present study confirms the association of TCF7L2 with type 2 diabetes with the largest effect size.The TCF7L2 gene product has been implicated in blood glucose homeostasis (5,30), and the variant rs7903146 is reported to be associated with measures of glucose metabolism (25).Consistent with these observations, we also found a strong association of TCF7L2 with HOMA-B and a nominal association with FPG and 2-h PPG, confirming the physiological role of TCF7L2 in glucose homeostasis.",
      "In summary, we have identified a variant in a previously unknown candidate gene for type 2 diabetes, TCF7L2, within a previously reported linkage region on 10q 1,8 .We have observed association of a composite at-risk allele of microsatellite DG10S478 within intron 3 of the TCF7L2 gene to type 2 diabetes in Iceland, which was subsequently replicated in Denmark and the US with similar frequency and relative risks.These data from three populations constitute strong evidence in support of the notion that variants of the TCF7L2 gene contribute to the risk of type 2 diabetes.",
      "TCF7L22.1.Background.The gene-encoding Transcription 7 Like-2 (TCF7L2, previously called TCF4) is the most important T2D susceptibility gene identified to date, with genetic variants strongly associated with diabetes in all major racial groups [27][28][29].Signals in this locus are the most consistently identified across various GWAS and are associated with the highest elevation of risk of developing adult-onset T2D.Each copy of the risk T-allele at rs7903146 has an increased odds ratio for T2D of 1.4-1.5 [60].Inheritance of the risk allele is also a useful predictor for the likelihood of conversion from a state of prediabetes to T2D [61,62].Additionally, results from a small number of studies also indicate that TCF7L2 variation may play an important role in cases of early onset T2D [63,64].",
      "One of the strongest T2DM risk-association in all the GWAS studies was found for common variants in TCF7L2, a gene coding for a transcription factor that is part of the WNT signaling pathway involved in the regulation of myogenesis and angiogenesis, but also critical for the embryonic development of pancreatic islets [19].Recently, it has been shown that the variant allele results in overexpression of TCF7L2 in pancreatic beta-cells, reducing insulin secretion in response to a variety of stimuli [6,8].The odd ratios (OR, is an estimate of the relative risk, with values [1.0 indicating a positive and \\1.0 a negative association, conferred by each additional risk allele carried at each locus) calculated in the pooled studies for the T allele in the snp7903146 of TCF/L2 was 1.37 (1.31-1.43)[13].This variant resides in an intron of the gene.Other variants at this locus also confer increased risk for T2DM, although the specific genetic defect that results in impaired insulin secretion in carriers has not been identified yet.Alternatively, other genes in the region may contribute to T2DM susceptibility.Associations between the T variant of TCF7L2 and T2DM have been consistently confirmed in geographically, ethnically, and environmentally diverse populations (references in [19], without evidence of heterogeneity across ethnic groups [2].",
      "The C to T (genomic position: 114748339) substitution at SNP rs7903146 of the intron 3 (IVS3C>T) is associated with T2DM and may function through impaired glucagon-like peptide 1 secretion, which is stimulated more by fat than by carbohydrate ingestion [25,26].TCF7L2 is present on chromosome 10q25, spanning 215.9 kb.It considered the most influential gene in determining the genetic susceptibility for T2DM today [27].TCF7L2 is the key transcriptional factor regulating glucose metabolism through the Wnt signaling pathway and has been reported to be critical for the development of the pancreas and islets during embryonic growth [3].Genetic variants in this gene are associated with increased risk of T2DM in a variety of study populations [28,29].",
      "The variants in TCF7L2, MC4R, CDC123, KCNQ1, IGF2BP2, and SLC16A11 have all been previously associated with T2D in adults (20,25).In addition, a prior study in SEARCH reported that genetic variation in TCF7L2 is associated with an increased risk of T2D in African American youth, with the OR for diabetes stronger in African American than in non-Hispanic White youth (12).The divergent ethnicity-based results did not replicate in ProDiGY, suggesting that the earlier findings in SEARCH might be due to statistical fluctuations in the context of smaller sample sizes."
    ]
  ],
  "task_id": [
    "8FFF9DCC307B8DBF2C8485637F2ABEF4",
    "C319861B08978CF5F7E6F0CD3A517A81",
    "68EF3BE5EC2106766CA9CC700135E2FA",
    "8590501C57DC5C321AB5E1036F233027",
    "CD1F7EAE0FDC758A8167118927ADFE71"
  ]
}