Type 2 diabetes is characterized by impaired insulin secretion and decreased insulin sensitivity. It has reached epidemic proportions worldwide and poses a considerable concern for public health. The management of this disease is hence strongly emphasized for the aim of controlling the epidemic. Oral and injectable pharmacologic agents are currently both available for glycemic control. Oral anti-diabetic drugs are among the most widely prescribed, including metformin, sulfonylureas, glinides, α-glucosidase inhibitors, thiazolidinediones and DPP-IV inhibitors [
1]. Despite the rapid progress in drug development, it is still challenging to achieve good glycemic control in a substantial population even when multiple anti-diabetic treatments are applied. As is well known, type 2 diabetes is a heterogenous disease, with clinical features, genetic risk factors and underlying pathogenic mechanisms varying among individuals [
2]. It is also well recognized that a great inter-individual variability exists in clinical outcomes of hypoglycemic agents. Therefore, poor therapeutic outcomes may be caused by treating patients without concern for the individual characteristics that might influence drug response. Since long-term hyperglycemia is an important contributor of micro- and macro-vascular complications, optimization of treatment strategies according to individual features, the so-called personalized medicine, is imperative.
Response of different individuals to pharmacotherapy may vary due to many factors such as age, gender, liver and/or kidney function and co-medications. It could also be partially attributable to polymorphisms in genes encoding drug-metabolizing enzymes, transporters, receptors and molecules involved in signal transduction. These variants may contribute to the variability in pharmacokinetics (drug absorption, distribution, metabolism and excretion) and pharmacodynamics (drug target, mechanism of drug action and drug response) of a specific drug, and thus lead to varied efficacious and toxic effects [
3]. Studies exploring such inherited differences have updated our knowledge, and the focus has been recently shifted from candidate genes (pharmacogenetics) to genome-wide association studies (pharmacogenomics). Pharmacogenetics/pharmacogenomics approaches are considered to be of importance in the promotion of personalized management of glycemia, helping physicians with the practice of a specific pharmacologic treatment for a genetically defined patient subset, by providing information for the decisions of drug selection, dose titration, treatment duration and avoidance of adverse drug reactions. In addition, they may also shed light on the mechanism of drug action and provide potential therapeutic targets.
Most of the pharmacogenetic studies of type 2 diabetes have focused on oral anti-diabetic drugs including sulfonylureas, glinides, metformin and thiazolidinediones. Numerous genetic markers have been identified so far, particularly those affecting drug disposition (pharmacokinetics). Genetic variants in genes encoding cytochrome P450 (CYP) enzymes contribute substantially to variability in drug efficacy or adverse reaction, as most of the oral hypoglycemic agents are metabolized by these enzymes in the liver. Defective alleles in these genes may be accociated with improved therapeutic effects of hypoglycemic agents and, however, may also be linked to increased incidence of adverse reactions. For example,
CYP2C9*2 and *3 were associated with impaired metabolism and reduced oral clearance of sulfonylureas. Patients carrying these alleles required lower doses of sulfonylureas, and were more likely to achieve glycemic goals including HbA
1c, but they were at a higher risk of mild or severe hypoglycemia, the major adverse effect of these drugs. In addition, variants in
CYP2C8 were found to be associated with efficacy of repaglinide, rosiglitazone and pioglitazone; while polymorphisms in
CYP2C9 affected nateglinide efficacy. With regard to the process of drug distribution, polymorphisms in genes encoding drug transporters also play important roles in drug efficacy. Variants in the
SLCO1B1 gene, which encodes organic anion transporting polypeptide 1B1 (OATP1B1), had impacts on therapeutic effects of repaglinide, nateglinide, rosiglitazone and pioglitazone. Moreover, researchers also found genetic determinants for metformin response in
SLC22A1 (encoding organic cation transporter 1) and
SLC22A2 (encoding organic cation transporter 2) [
4].
Compared with the rapid development in pharmacokinetics, genetic variants influencing pharmacodynamics of hypoglycemic agents are less well characterized. Studies investigating polymorphisms in genes encoding drug targets have led to the identification of some pharmacogenetic markers.
KCNJ11 and
ABCC8 were found to be harboring genetic markers for sulfonylureas, and
PPARg harboring genetic variants for thiazolidinediones. Other pharmacodynamic components have also been identified. For instance, variants located in
IRS1 and
NOS1AP were correlated with sulfonylureas response [
4]. To date, more than 60 loci have been identified to confer susceptibility to type 2 diabetes, but the contribution of each locus to disease risk is relatively small. Each diabetes-related locus alone or the combination of all the susceptible loci is thought to have the potential to direct individualized therapy of type 2 diabetes. Therefore, there are attempts to assess the effects of type 2 diabetes-related gene polymorphisms on treatment outcome of anti-diabetic drugs. Such attempts have linked
TCF7L2 to sulfonylureas efficacy [
4],
TCF7L2,
KCNJ11,
KCNQ1,
IGF2BP2,
SLC30A8 to therapeutic outcome of repaglinide [
5-
9], and so on.
Despite the accumulating pharmacogenetic evidence for anti-diabetic medications, pharmacogenomics of these drugs remains in the preliminary stage. As a hypothesis-free approach, genome-wide association study investigates SNPs covering the whole genome, and is regarded as a good biological tool for the identification of genetic markers for treatment outcome. Recently, the first pharmacogenomic study in this field has identified a novel genetic variant (rs11212617), located at a locus containing
ATM gene, to be associated with glycemic response to metformin in type 2 diabetes in European populations [
10]. This study established the utility of a genome-wide association approach to investigate pharmacogenomics in type 2 diabetes and set an example for future pharmacogenomic research of hypoglycemic therapies. These data also suggest that by such an unbiased way, new biological pathways involved in drug response could be uncovered.
Most of the genetic markers for hypoglycemic agents were identified in populations of European ancestry, whereas the pharmacogenomic advances in Chinese populations are relatively limited. Previous studies have demonstrated the association of CYP2C9, CYP2C19 and ABCC8 with response to sulfonylureas therapy; the association of PGC-1a, UCP-2, ACRB3, SLC30A8 and ABCA1 with rosiglitazone treatment outcome; the association of PPARg, ADIPOQ, LPL with pioglitazone efficacy; the association between SLCO1B1 and nateglinide efficacy; the effect of SLC22A2 on pharmacokinetics of metformin in Han Chinese. It is of note that researchers have paid great attention to the pharmacodynamics of repaglinide in patients of Chinese ethnicity in recent years. Numbers of genetic markers were thus found to be associated with repaglinide efficacy, including KCNQ1, NOS1AP, KCNJ11, ABCC8, NAMPT, IGF2BP2, TCF7L2, MDR1, UCP2, SLC30A8, NeuroD1 and PAX4.
Clinical application of pharmacogenomic information has been well established in monogenic diabetes. One of the most stunning examples is the genetic testing for mutations in
KCNJ11 in patients with neonatal diabetes.
KCNJ11 gene encodes the Kir6.2 subunit of K
ATP channel in pancreatic b cells, and mutations of this gene are present in approximately half of the neonatal diabetes patients. Most of these patients were treated with insulin previously. A great breakthrough occurred when investigators tried to apply sulfonylureas in place of insulin. Effective management of blood glucose concentrations and glycated hemoglobin levels was achieved after transition to sulfonylureas, even after treatment with insulin for many years [
11,
12].
Unlike those involved in monogenic forms of diabetes, genotypic markers for polygenic diabetes including type 2 diabetes are still far from routine clinical practice. This may be partly due to the following limitations in most of the available data at present. Firstly, the sample size is relatively small, which implies insufficient statistical power of these studies. Secondly, data presented in most of the studies are from healthy volunteers, and the observations established in these studies could thus not be directly implemented to patients with type 2 diabetes. Thirdly, there are no strict definitions for drug response and incidence of adverse drug reactions. All these limitations have hampered the translation of these findings to clinical practice. Prospective clinical trials, demonstrating the usefulness of such testing, are now regarded as strategies to overcome all these limitations. Therefore, need for large, well-designed and well-powered prospective studies will be highlighted in the future [
4]. Moreover, although genotypic markers can serve as complementary tools that can be used together with blood glucose and HbA
1c monitoring, algorithms incorporating pharmacogenomic information should be constructed before applying these markers.
In addition to pharmacogenomic markers, numerous molecular biomarkers including markers derived from proteomics and metabolomics, have been largely investigated in recent years. Proteomics is an approach to examine large numbers of proteins in a body fluid or tissue extract, by which the abundance of proteins and their functional status can be directly assessed. Metabolomics mainly aims at determining metabolite profiles in a specific disease state or for an individual, by measuring a comprehensive set of metabolites in body fluids or tissue extracts. Both approaches may have great potential in providing new insights into pathophysiology of type 2 diabetes and identifying useful biomarkers for personalized medicine of type 2 diabetes. However, proteomic or metabolomic data have not been applied in individualized diabetes management yet. In future, molecular markers identified by these approaches can be used in combination with pharmacogenomic markers to direct treatment decisions. There is also suggestion that epigenetic information should be integrated into personalized management of type 2 diabetes. Whether these new approaches can guide individualized therapy requires substantial further investigation.
Higher Education Press and Springer-Verlag Berlin Heidelberg
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