Metabolomic and Genomic Profiling in Diabetic Medicine
Metabolomic and Genomic Profiling in Diabetic Medicine
Despite many candidate gene studies and genome-wide linkage studies, very few susceptibility loci for type 2 diabetes have been identified until the recent emergence of genomic-wide association (GWA) data and large-scale replication studies (Table 2). Meta-analysis of GWA studies provides the unique opportunity to investigate the heterogeneity or consistency of genomic associations across diverse datasets and study populations. Recently, Voight et al., using large-scale association analyses combining the data from eight GWA studies, identified 12 new susceptibility loci for type 2 diabetes.
Despite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes. Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes. Candidate gene studies have also reported many type 2 diabetes–associated loci and the coding variants in the nuclear receptor peroxisome proliferator–activated receptor-γ, the potassium channel KCNJ11,WFS1, and HNF1B (TCF2) are among the few that have been replicated (Table 2). Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to high-throughput genotyping technologies, the International HapMap Project, and the Human Genome Project. Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 are among some of the established loci (Table 2). CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2). GWA studies have also identified the potassium voltage-gated channel KCNQ1 as an associated gene variant for diabetes. A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641. Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted. GWA studies for glycemic traits have identified loci such as MTNR1B,GCK (glucokinase), and GCKR (glucokinase receptor); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed. Indeed, a recent study by Soranzo et al. using GWA studies identified ten genetic loci associated with HbA1c. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA1c.
Significant effects of many susceptibility loci are still to be determined and replicated, and further large-scale association studies will be required. Recently, Schleinitz et al. found some of the type 2 diabetes risk alleles or related subphenotypes to be weak, including those of JAZF1, CDC123/CAMK1D, NOTCH2, ADAMTS9, THADA, and TSPAN8-LGR5. The TNF/LTA locus has been a long-standing type 2 diabetes candidate gene, whereas a recent study found no evidence of an association between TNF/LTA region variation and type 2 diabetes. The association of polymorphisms in TNFa and type 2 diabetes has been extensively reported. Recently, the TNFa variant rs3093662, linked to higher serum levels of tumor necrosis factor-α, was shown to be associated with elevated insulin. Mutated transcription factors, hepatocyte nuclear factor (HNF)1A and HNF4A, have received substantial attention, and there is evidence for susceptibility of the variants to maturity-onset diabetes of the young (MODY) and type 2 diabetes. Recently, high-sensitivity CRP was shown to discriminate HNF1A-MODY from other subtypes of diabetes.
Interestingly, many of the established susceptibility loci are involved in insulin secretion signaling, supporting an important role for defects in β-cell function and β-cell mass in type 2 diabetes. The exciting potential of genetic testing for susceptibility of diabetes appears to be some way off, apart from rare forms of monogenic diabetes. Moreover, it is well known that nongenetic factors such as obesity and lifestyle factors play an important role in the disease. New phenotyping approaches to studying metabolite and protein abundance and data integration are needed to bring genomic and metabolomic goals together. In this context, the Human Metabolome Project in Canada, aimed at providing a linkage between the human metabolome and the human genome, has identified and quantified normal concentration ranges for a large number of metabolites in cerebrospinal fluid, serum, urine, and other tissues and biofluids. There are currently 7,900 entries in the Human Metabolome Database (http://www.hmdb.ca), which contains detailed information about small molecule metabolites and will be useful for applications in metabolomics, clinical chemistry, and biomarker discovery.
Genomic Variations and DNA Profiling of Those at Risk for Type 2 Diabetes
Despite many candidate gene studies and genome-wide linkage studies, very few susceptibility loci for type 2 diabetes have been identified until the recent emergence of genomic-wide association (GWA) data and large-scale replication studies (Table 2). Meta-analysis of GWA studies provides the unique opportunity to investigate the heterogeneity or consistency of genomic associations across diverse datasets and study populations. Recently, Voight et al., using large-scale association analyses combining the data from eight GWA studies, identified 12 new susceptibility loci for type 2 diabetes.
Despite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes. Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes. Candidate gene studies have also reported many type 2 diabetes–associated loci and the coding variants in the nuclear receptor peroxisome proliferator–activated receptor-γ, the potassium channel KCNJ11,WFS1, and HNF1B (TCF2) are among the few that have been replicated (Table 2). Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to high-throughput genotyping technologies, the International HapMap Project, and the Human Genome Project. Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 are among some of the established loci (Table 2). CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2). GWA studies have also identified the potassium voltage-gated channel KCNQ1 as an associated gene variant for diabetes. A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641. Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted. GWA studies for glycemic traits have identified loci such as MTNR1B,GCK (glucokinase), and GCKR (glucokinase receptor); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed. Indeed, a recent study by Soranzo et al. using GWA studies identified ten genetic loci associated with HbA1c. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA1c.
Significant effects of many susceptibility loci are still to be determined and replicated, and further large-scale association studies will be required. Recently, Schleinitz et al. found some of the type 2 diabetes risk alleles or related subphenotypes to be weak, including those of JAZF1, CDC123/CAMK1D, NOTCH2, ADAMTS9, THADA, and TSPAN8-LGR5. The TNF/LTA locus has been a long-standing type 2 diabetes candidate gene, whereas a recent study found no evidence of an association between TNF/LTA region variation and type 2 diabetes. The association of polymorphisms in TNFa and type 2 diabetes has been extensively reported. Recently, the TNFa variant rs3093662, linked to higher serum levels of tumor necrosis factor-α, was shown to be associated with elevated insulin. Mutated transcription factors, hepatocyte nuclear factor (HNF)1A and HNF4A, have received substantial attention, and there is evidence for susceptibility of the variants to maturity-onset diabetes of the young (MODY) and type 2 diabetes. Recently, high-sensitivity CRP was shown to discriminate HNF1A-MODY from other subtypes of diabetes.
Interestingly, many of the established susceptibility loci are involved in insulin secretion signaling, supporting an important role for defects in β-cell function and β-cell mass in type 2 diabetes. The exciting potential of genetic testing for susceptibility of diabetes appears to be some way off, apart from rare forms of monogenic diabetes. Moreover, it is well known that nongenetic factors such as obesity and lifestyle factors play an important role in the disease. New phenotyping approaches to studying metabolite and protein abundance and data integration are needed to bring genomic and metabolomic goals together. In this context, the Human Metabolome Project in Canada, aimed at providing a linkage between the human metabolome and the human genome, has identified and quantified normal concentration ranges for a large number of metabolites in cerebrospinal fluid, serum, urine, and other tissues and biofluids. There are currently 7,900 entries in the Human Metabolome Database (http://www.hmdb.ca), which contains detailed information about small molecule metabolites and will be useful for applications in metabolomics, clinical chemistry, and biomarker discovery.
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