Underlying Mechanisms and Clinical Consequences

1.1. Monogenic Obesity

Initial knowledge on the genetic involvement in monogenic obesity was derived from large-scale linkage analysis in obese mice carrying naturally occurring mutations. These analyses have pointed to disease-related loci and have identified the majority of gene mutations leading to monogenic obesity in mice (3). In particular, the genetic characterization of naturally occurring obese animal models, such as ob/ob, db/db, fat and tubby mice, led to the discovery of recessive mutations in the genes encoding leptin (Lep or ob), leptin receptor (Lepr or db), carboxypeptidase E (Cpe, or fat), and tubby (Tub) (5,6). Furthermore, the latest murine obesity gene map identified 248 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes affecting body weight and adiposity (7). Transfer of this knowledge to clinical cases has confirmed the role of the above genes in human monogenic obesity and uncovered the critical role of the leptin/melanocortin pathway in the regulation of energy homeostasis (8). Briefly, this hypothalamic pathway is activated following the systemic release of leptin and its subsequent interaction with the leptin receptor located on the surface of neurons of the arcuate nucleus of the hypothalamus. The downstream signals that regulate energy homeostasis are then propagated via proopiomelanocorin (POMC), cocaine- and amphitamine-related transcript (CART) and the melanocortin system (9,10) . While POMC/CART neurons synthesize the anorectic peptide α -melanocyte-stimulating hormone (α-MSH), a separate group of neurons express the orexigenic neuropeptide Y (NPY) and the agouti-related protein, which acts as a potent inhibitor of melanocortin 3 receptor (MC3R) and melanocortin 4 receptor (MC4R).

To date, mutations in 11 different genes (Table 1 ), including LEP, LEPR, POMC, and proconvertase 1 (PC1), have been linked to obesity, in nearly 200 patients (7,30). Patients with monogenic obesity have extremely severe phenotypes that present in childhood and are often associated with additional behavioral, developmental, and endocrine disorders (31). MC4R-linked obesity represents the most prevalent form of

Table 1

Genes Implicated in Monogenic Obesity

monogenic obesity identified to date, representing ~2–3% of childhood and adult obesity (30,32,33). MC4R is a G-protein-coupled receptor with seven transmembrane domains that plays an important role in controlling weight homeostasis (10). MC4R knockout mice develop morbid obesity and increased linear growth, whereas heterozygous mice are also obese but with a varying degree of severity (34). Investigations in the molecular mechanisms by which loss of function mutations in MC4R cause obesity have suggested a number of functional anomalies, including abnormal MC4R membrane expression, a defect in agonist response, and disruption in the intracellular transport of the protein (35). Other single gene mutations leading to obesity involve single-minded homolog 1 (SIM1), melanocortin receptor 3 (MC3R), and neurotrophic tyrosine kinase receptor type 2 (TRKB/NTRK2) (23,24,27).

The major goal of the extensive ongoing research is the development of therapies targeting monogenic obesity, in order to ameliorate the metabolic status of obese individuals. Leptin therapy, by subcutaneous injection of leptin in children and adults deficient in this adipokine, markedly reduced their body weight, having a major effect on reducing food intake and on other dysfunctions, including immunity (36) . Although treatments are not available yet for cases of LEPR, POMC-, PC1-, SIM1-, MC4R-, and TRKB-linked obesity, preliminary studies suggest that targeted therapies could be possible to develop (37).

1.2. Syndromic Obesity

In addition to the monogenic forms of obesity, this phenotype is also associated with many genetic syndromes. Syndromic obesity was initially thought of as monogenic; however, the contribution of multiple genetic factors in a syndrome is significantly more challenging than localizing the single gene involved in monogenic disorders.

There are currently 20–30 Mendelian disorders in which, in addition to mental retardation, dysmorphic features, and organ-specific developmental abnormalities, patients are also clinically obese (30,31). Such cases are referred to as syndromic obesity. These syndromes arise from discrete genetic defects or chromosomal abnormalities and can be either autosomal or X-linked disorders. The most common disorders known are Prader– Willi syndrome (PWS), Bardet-Biedl syndrome (BBS), and Alström syndrome (38).

PWS, the most frequent of these syndromes (1 in 25,000 births), is characterized by obesity, hyperphagia, diminished fetal activity, mental retardation, and hypogonadism. PWS is caused by the absence of the paternal segment 15q11.2–q12, through chromosomal loss (39–41). Several candidate genes in this chromosomal region have been studied; however, the genetic basis of polyphagia remains undefined because none of the PWS mouse models have an obese phenotype (42). One genetic candidate that may disrupt the control of food intake is the gastric hormone ghrelin, which could act through the regulation of hunger and stimulation of growth hormone (43).

BBS is characterized by early onset obesity, retinal dystrophy, morphological fi nger abnormalities, mental disabilities, and kidney diseases (44,45). To date, BBS has been associated with at least 12 distinct chromosomal locations, with several mutations identified so far (46–57). Although the precise function of the BBS proteins is yet to be determined, current data support a role in cilia function and intrafl agellar transport (58–60).

Alström syndrome is a very rare disorder, which in addition to obesity, is associated with congenital retinal cone dystrophy, cardiomyopathy, and type 2 diabetes (61,62) . Family studies have identified several mutations in the Alström syndrome 1 gene ( ALMS1 ), the majority of which are nonsense and frameshift (insertion or deletion) mutations predicted to lead to premature protein termination (63–65). ALMS1 is a ubiquitously expressed protein with recently proposed functional involvement in cilia formation (66,67).

As the above genetic syndromes involving obesity are rare, their underlying genetic involvement has been difficult to decipher. Furthermore, even in the cases where the responsible genes have been identified, the pathophysiological link between the protein products and the development of the disease has not yet been fully elucidated.

1.3. Polygenic Obesity

Polygenic, or common, obesity arises when an individual’s genetic makeup is susceptible to an environment that promotes energy intake over energy expenditure. Specifically, environments in most westernized societies favor weight gain rather than loss because of food abundance and lack of physical activity, thus rendering common obesity as a major epidemic currently challenging the medical and financial resources in these societies (37).

A range of polygenic mouse models have been generated through inbreeding of mouse lines or repeated selections of noninbred mice, and have enabled the identifi cation of >408 quantitative trait loci (QTL) associated with obesity ( http://obesitygene. pbrc.edu ). A recent meta-analysis of ~280 QTL, from 34 mouse cross-breeding experiments involving >14,500 mice, revealed 58 QTL regions associated with body weight and adiposity ( http://www.obesitygenes.org ) (68). Different QTL have been associated with the age of onset and gender in obesity, while certain loci may only contribute to obesity by interacting with other loci (69).

In humans, studies of polygenic obesity are based on the analysis of single nucleotide polymorphisms (SNPs) or repetition of bases (polyCAs or microsatellites) located within or near a candidate gene. These studies are carried out in family members (family study) or unrelated individuals (case–control study), and their objective is to determine a potential association between a gene’s allelic variant and obesity-related traits (70). However, unlike monogenic obesity, many genes and chromosomal regions contribute to the common obese phenotype (7,71). For this purpose, large DNA banks have been established from different populations throughout the world and are used for the extensive investigation of large number of genes and chromosomal regions. The findings of these genetic studies are reported every year by the Human Obesity Gene Map consortium. According to their latest report, 253 QTL have been identifi ed, in 61 genome-wide scans (7). All chromosomes, except the Y chromosome, have been found linked with an obesity-related phenotype, such as fat mass, distribution of adipose tissue, resting energy expenditure, or levels of circulating leptin and insulin. Genes associated with obesity include solute carrier family 6 (neurotransmitter transporter) member 14 (SLC6A14), glutamate decarboxylase 2 (GAD2), and ectonucleotide pyrophosphatase/ phosphodiesterase I (ENPPI) (72–74). These genes have been implicated in a variety of biological functions such as the regulation of food intake, energy expenditure, lipid and glucose metabolism, adipose tissue development, and inflammatory processes. Recent genome-wide association studies have identified genetic variants (SNPs) associated with obesity-related traits in both children and adults, in the fat mass and obesity associated (FTO) gene (75–77, 272). It has been proposed that through its catalytic activity, FTO may regulate the transcription of genes involved in metabolism (78).

In contrast to genetically identical mice, whose environments can be controlled, the genetic and environmental diversity in humans has proved problematic for data replication. To date, only 22 obesity-related genes are supported by at least five positive studies (7,37). The reasons for the lack of replication in association and linkage studies include lack of statistical power to detect modest effect, lack of control over type I error rate, and overinterpretation of marginal data (79). Thus, the use of novel approaches may provide the means to circumvent classical statistical obstacles in identifying new candidate genes and possible gene–environment interactions (see Sect. 4).

The immense ongoing research on the identification of new molecular targets for antiobesity drugs and the significance of the generated findings is reflected by the rapidly increasing number of patent applications. Specifically, a total of 173 US patents were issued between January 2001 and March 2004, with the word “obesity” included in the abstract (80,81). Among the molecular targets with the highest number of new patents are the serotonin receptor ligands (24 patents), neuropeptide Y receptor ligands (20 patents), and adrenergic receptor ligands (20 patents).

2.1. The Metabolic Syndrome

The term metabolic syndrome (occasionally called insulin resistance syndrome) refers to a constellation of clinical findings including obesity, hypertension, hyperlipidemia, and insulin resistance, with increased risk for type 2 diabetes and cardiovascular disease. It has also been linked with chronic kidney disease, liver disease with steatosis, fibrosis, and cirrhosis, and cognitive decline and dementia. Despite recent controversy regarding the concept of a metabolic syndrome, the International Diabetes Federation (IDF) developed a new unifying worldwide definition building upon the World health(Buy now from http://www.drugswell.com) Organization (WHO) and ATP III definitions, as will be discussed in later chapters (82).

On the basis of the IDF definition, almost 40% of US adults are classified as having the metabolic syndrome (83). Although environmental factors such as smoking, low economic status, high intake of carbohydrates, no alcohol consumption, and physical inactivity can play a role in the development of the metabolic syndrome, a series of evidence indicates that there is also a genetic component involved. Specifically the metabolic syndrome has different prevalence between men and women, and among ethnic groups, as well as different concordance rates between monozygotic twins. Furthermore, there is increased incidence in individuals with a parental history of metabolic syndrome, and a general familial clustering of the metabolic syndrome and its components (83–91).

Ongoing work on spontaneous and engineered animal models has revealed that several genetic loci are associated with metabolic syndrome components in different rodent models (92). Examples of metabolic syndrome rodent models include the spontaneous hypertensive rat (SHR), the transgenic SHR overexpressing a dominant-positive form of the human sterol regulatory element binding transcription factor 1 (SREBP-1), the SHR/ NDmcr-cp rat, the polydactylous rat strain (PD/cub), the obese Zucker rats (OZR), the New Zealand obese (NZO), the Wistar Ottawa Karlsburg W rats, as well as congenic, consomic, and double-introgressed strains (93–100).

Linkage analyses in patients with the metabolic syndrome have aimed at identifying loci with pleiotropic effects on multiple aspects of the syndrome. Several different linkage analysis approaches have been applied in the study of the metabolic syndrome, such as principal components or principal factor analysis, multivariate analysis, metabolic syndrome score from combined residuals and the structural equation model (101). One of the most consistent findings was the linkage to chromosome 1q, while multiple phenotypes linked to this region indicate that it likely harbors a gene with pleiotropic effects on measure of glucose, lipids, hypertension, and adipocity, or multiple genes that contribute to each one of these features (102–106). Other consistent loci implicated in the development of the metabolic syndrome include chromosomes 2p, 2q, 3p, 6q, 7q, 9q, and 15q (103,106–111).

Many of these loci have also been linked to individual components of the metabolic syndrome. For example, chromosome 2p has been linked to serum triglycerides, systolic blood pressure, obesity, body fat percentage, and HDL (111–113) , while chromosome 7q has been linked to systolic blood pressure, triglyceride–HDL-C ratio, fasting glucose, insulin, and insulin resistance (114–116).

Despite the wide use and important findings that have emerged from linkage analysis, this method presents with a number of limitations that need to be carefully considered and addressed in the interpretation of current findings and the design of future studies. Some of the common obstacles in this type of studies are the inadequate statistical power, the multiple hypothesis testing, the population stratification, the publication bias and phenotypic variation (117) . The identification of true genetic associations in common multifactorial conditions, such as the metabolic syndrome, requires large studies consisting of thousands of subjects. This need is further accentuated by the large number of implicated genetic loci and their potentially small contribution to the phenotype when individually considered.

In parallel to linkage and association studies, several studies have evaluated the contribution of specific candidate genes to the metabolic syndrome pathogenesis. These candidate genes have been selected based on their biological function and/or previous associations to any of the phenotypic aspects of the syndrome. However, the large number of metabolic pathways implicated in the pathogenesis of the metabolic syndrome (including insulin signaling, glucose homeostasis, lipoprotein metabolism, adipogenesis, inflammation, coagulation, etc.) renders this search a highly challenging task that has yielded a relatively limited success. There are many examples of genes directly or indirectly implicated in the development of the metabolic syndrome or specific clinical features related to it, but an equal number of negative studies have also been published (118).

The peroxisome proliferator-activated receptor γ (PPAR g) is one of the strong candidates for conferring susceptibility to the metabolic syndrome because of its involvement in adipocyte differentiation, fatty acid metabolism, insulin sensitivity, and glucose homeostasis (119–121). Despite some inconsistencies in the PPAR γ association studies, the overall evidence seems to suggest that PPAR g polymorphisms can increase the risk for developing the metabolic syndrome (122–124). Direct correlations to the metabolic syndrome have also been described for genetic variants of the β₃ -adrenergic receptor (ADRb-3), nitric oxide synthase 3 (NOS3), angiotensin I converting enzyme (ACE), beacon (BEACON), lamin A/C ( LMNA), interleukin-6 (IL-6 ), interleukin- β (IL1-b ), and protein tyrosine phosphatase nonreceptor type 1 (PTPN1) genes (122,125–131) . Interestingly, PPAR g and IL1-b polymorphisms have been implicated in gene–environment interactions (see Sect. 4 ).

Fatty acid binding protein 2 (FABP2) and apolipoprotein C-III (APOC3 ) polymorphisms have been directly associated with increased risk for dyslipidemia and the metabolic syndrome in Asian-Indians (132). Other examples include a number of lipid-sensitive transcription factors (nuclear receptor subfamily 1, member 4 (FXR ), nuclear receptor subfamily 1, member 3 (LXR-a), retinoid X receptor α (RXR-a), PPAR- a, PPAR- d, peroxisome proliferator-activated receptor ( PGC1-a), PCG1-b, sterol regulatory element binding transcription factor 1 (SREBP-1c)) that have been implicated in the development of dyslipidemia, one of the very early features of the metabolic syndrome (124). Since lipoprotein metabolism plays a central role in the metabolic syndrome, several genes related to the former are also good candidates for the latter. These include variants of scavenger receptor class B, member 1 (SCARB1 ), ATP-binding cassette subfamily A, member 1 ( ABCA1), cholesteryl ester transfer protein (CETP), lipoprotein lipase (LPL), lipase (LIPG), pancreatic lipase (PNLIP ), apolipoprotein A-V (APOA5), and the apolipoprotein gene clusters ApoA1/C3/A4/A5 and ApoE/C1/C2 that affect HDL-cholesterol and triaglyceride metabolism (133–138).

2.2. Hypertension

Hypertension is one of the components of the metabolic syndrome and a major risk factor for cardiovascular disease. Similar to obesity and the metabolic syndrome, hypertension seems to be the outcome of combined genetic and environmental etiologies (139). Mutations in eight genes have been identified to cause severe but rare forms of monogenic hypertension (140). Interestingly, all of these genes participate in the same physiological pathway in the kidney, altering net renal salt reabsorption. However, the genetic factors behind the common, less severe forms of hypertension, collectively termed essential hypertension (i.e., hypertension with unknown cause), are poorly understood. A large number of candidate gene, linkage, and association studies have sporadically implicated a range of different genetic loci in hypertension development. Polymorphisms in the angiotensinogen (AGT), the natriuretic peptide receptor A ( NRP1), and ACE are prime examples of the most consistent findings in the literature (141–144) . Nonetheless, genome-wide linkage analyses have not consistently implicated specific chromosomal loci, suggesting a model in which there may be many loci, each imparting small effects on hypertension in the general population (145–148). Similar to other multifactorial diseases, the study of hypertension in humans will require the consistent replication of results in large and rigorously characterized populations that are well suited for detecting alleles imparting small effects. Such populations would include cohorts of unrelated individuals as well as family-based linkage disequilibrium studies. These latter tests minimize the chance of false-positive associations arising from population admixture of individuals of different genetic backgrounds (149). Meta-analysis of the combined results from multiple different studies/populations can also greatly contribute towards this end, as for example in the case of a methylenetetrahydrofolate reductase (MTHFR) polymorphism that appears to be significantly associated with hypertension in multiple populations (150).

In parallel to human studies, a series of spontaneous and engineered animal models of hypertension have been extensively studied. For example, inbred rat strains that display hypertension as an inherited trait have long been used as a means for identifying genes that can give rise to essential hypertension. Examples of these strains include SHRs, Dahl salt-sensitive rats, Sabra hypertensive-prone rats, Molan, Lyon, fawn-hooded and Prague hypertensive rats (151). Importantly, some of the fi ndings in these animal models have later been translated to humans, such as in the case of brain and muscle Arnt-like protein-1 ( Bmal1) polymorphisms which are associated with susceptibility to hypertension and type 2 diabetes (152). Congenic and consomic rat strains have also been used to identify QTL for hypertension, in an effort to eliminate the variability arising from the often heterogeneous genetic background of these animals (151,153–157). In support of the notion that hypertension is a polygenic condition, at least one blood-pressure-related QTL has been identified on almost all rat chromosomes (151). Genetically engineering mouse models with increased or decreased expression of targeted genes has also provided useful insights (158) . For example, deletions of various genes (including the bradykinin B2 receptor, D1A and D3 dopamine receptors, atrial natriuretic peptide, endothelial nitric oxide synthase, and others) have resulted in elevated blood pressure, while in other cases, gene mutations have had little or no effect (159–163). Furthermore, mouse models have enabled the confirmation of various observations in humans, and the more detailed characterization of the disease physiology (158).

2.3. Type 2 Diabetes

Diabetes mellitus represents a group of metabolic disorders characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The pathogenic processes involved in the development of diabetes range from autoimmune destruction of the pancreatic β cells with consequent insulin deficiency to abnormalities that result in resistance to insulin action (164). There are two main etiopathogenetic categories of diabetes: (1) type 1 diabetes, which is caused by deficiency of insulin secretion and rises independently of obesity or the metabolic syndrome (will be covered in Sect. 3 ), and

(2) type 2 diabetes, which is caused by a combination of resistance to insulin action and inadequate compensatory insulin secretion. Type 2 diabetes, or noninsulin-dependent diabetes mellitus, is the most frequent form of diabetes, accounting for 90% of the disease prevalence, with an estimated 150 million affected people worldwide (165,166). Overall, type 2 diabetes is characterized by impairment of insulin secretion and decrease in insulin sensitivity. Initial studies in families with rare monogenic forms of diabetes pointed towards a genetic component of type 2 diabetes (167). However, it has become evident that the incidence of the disease is also affected by environmental influences, such as lifestyle and diet.

On the basis of the role of genetic factors, type 2 diabetes may be divided into monogenic and polygenic forms, where monogenic forms are the consequence of rare mutations in a single gene whereas polygenic forms are the result of the interaction between the environment and genetic contribution of many different genes (168,169).

2.3.1. Polygenic Type 2 Diabetes

Polygenic, or the common form, type 2 diabetes is a complex and heterogeneous disorder that is influenced by the contribution/impact of multiple genes and various environmental factors that can affect disease predisposition. In many cases obesity and the metabolic syndrome precede the development of type 2 diabetes. Owing to its complexity, with both gene–gene and gene–environment interactions, the genetic influences on this form of type 2 diabetes have been difficult to elucidate and the identification of genes has not been easily achieved (Fig. 1 ).

Animal models for type 2 diabetes have enabled the study of the molecular pathways involved in disease pathophysiology, providing useful information on the molecular etiology of type 2 diabetes and pointing towards potential therapeutic interventions. The numerous spontaneous animal models for type 2 diabetes have facilitated our understanding of disease physiology and have aided towards the identification of underlying genetic factors. Examples of such models include the Nagoya-Shibata-Yasuda (NSY) mouse model, which spontaneously develops diabetes in an age-dependent manner, the diabetic db/db mice and the KK mouse strain, which shows inherently glucose intolerance and insulin resistance (170–172). Additional spontaneous animal models presenting insulin resistance and impaired insulin secretion include the Goto Kakizaki rat, the Otsuka Long-Evans Tokushima fatty (OLETF) rat and the Zucker Diabetic Fatty rat model (173–175). Genome-wide linkage scans in OLETF rats have identified susceptibility loci on chromosomes 1, 7, 14, and the X chromosome, while a sequence variation in the hepatocyte nuclear factor 1β (Hnf1b), a gene implicated in human MODY (maturity-onset diabetes of the young) disease, was identified in the NSY mouse model (176–178).

In addition to spontaneous animal models, an increasing number of genetically engineered models have been generated for type 2 diabetes. In an attempt to recreate the human disease in animals, investigations have focused on the understanding of β-cell dysfunction or insulin resistance pathways. Depending on the targeted protein and its importance on insulin signaling, various degrees of insulin resistance can be created. Insulin-receptor (IRS)-deficient mice were among the first knockout mice to be generated with affected proteins in the insulin signaling cascade. Heterozygous mice exhibit normal glucose tolerance and only 10% of adult animals develop diabetes, while homozygous

Fig. 1. Progress in the identifi cation of susceptibility genes for type 1 and type 2 diabetes over the past decade.

IRS-deficient mice rapidly develop diabetes and die within 3–7 days after birth, thus demonstrating the essential role of IRS in the control of glucose metabolism (179,180). Deficiency of the insulin receptor substrate 1 protein (IRS-1) in mice results in postnatal growth retardation with only mild insulin resistance and no diabetes, whereas deletion of IRS-2 causes impaired insulin signaling and β-cell function, resulting in progressive deterioration of glucose metabolism (181,182). On the other hand, IRS-3 and IRS-4 knockout mice show respectively either mild glucose intolerance or have no phenotype, therefore suggesting that they are unlikely to play a major role in glucose homeostasis (183,184).

In an attempt to resemble the polygenic nature of type 2 diabetes, polygenic animal models containing combined gene disruptions have been created. Double heterozygous mice for IRS and IRS-1 exhibit a synergistic impairment on insulin action, presenting a phenotype that is much stronger than individual gene defi ciency (185). In contrast to their respective individual gene deficiency models, double knockout mice for IRS-1 and β-cell glucokinase (Gck) develop overt diabetes, demonstrating that combination of minor mutations in genes involved in either insulin action alone or insulin secretion and action can cause diabetes (186). Overall, polygenic mouse models have demonstrated that, when combined, minor defects in insulin secretion and action can lead to diabetes, therefore emphasizing the interaction between different genetic loci in diabetes.

Animal models with tissue-specifi c inactivation of insulin receptor genes have also been generated, in order to assess insulin action in individual tissues. These include the muscle-specific insulin receptor knockout mice, the liver insulin receptor knockout mice, and the β-cell insulin receptor knockout mice (187–189). Such tissue-specifi c models have helped in dissecting the contribution of individual insulin-responsive organs to glucose metabolism.

In humans, candidate gene analyses towards the identifi cation of type-2–diabetesrelated genes have focused on genes implicated in insulin resistance and particularly in β-cell development, insulin signaling, or hypothalamic regulation. This has included genes such as the PPAR g, the ATP-binding cassette subfamily C member 8 ( ABCC8 ) and potassium-inward rectifier 6.2 ( KCJN11), and IRS-1 (119,190) . The best-characterized and most robust variant is the highly prevalent Pro21Ala polymorphism in PPAR g . Two meta-analyses have shown that the proline allele, which is the most frequent allele, is associated with a moderate increase in risk for type 2 diabetes. Furthermore, a 21–27% risk reduction was shown for the presence of the alanine allele, hence suggesting that the alanine genotype results in greater insulin sensitivity (191–193) .Other meta-analyses studies have determined that in the KCJN11 gene, which encodes the ATP-sensitive potassium channel subunit Kir6.2, the frequent variant E23K shows association with a slightly increased susceptibility to type 2 diabetes in some populations, with the risk for the disease increasing by about 15% in the presence of the K allele (190,194) . However, in many cases the initial associations have not been replicated in subsequent studies. For example, a meta-analysis of ~9,000 individuals initially determined that the G971R variant in IRS-1 had a significant effect on diabetes risk; however, two subsequent studies failed to confirm this association (195–197).

To date, more that 50 linkage studies have been conducted in a variety of populations. Although initially the regions of linkage determined by the different studies were inconsistent (because of differences in study design, family confi guration, ethnic heterogeneity), the completion of additional scans revealed that some chromosomal regions, and in particular chromosomes 1q21–24, 1q31–q42, 9q21, 10q23, 11p15, 12q12, 19q13, and 20q11–q13, are showing positive association with the disease in more than one study (198). Calpain 10 (CAPN10) was the first polygenic diabetes gene to be cloned

(199) and it encodes for a ubiquitously expressed cysteine protease. Although widespread acceptance of CAPN10 as a type-2-diabetes-predisposing gene was not initially achieved, recent studies have provided further evidence for the biological importance of CAPN10 variation in susceptibility for the disease. A meta-analysis of more than 7,500 patients of diverse ethnic origin has determined a significant association for the presence of a CAPN10 variant (SNP-44; CAPN10-g4841 T → C) and the disease (200) . It has been proposed that genetic variants of CAPN10 might affect insulin sensitivity, insulin secretion, or the relation between the two (201–203). Other genes associated with the common form of type 2 diabetes include transcription factor 7-like 2 gene (TCF7L2) (204,205), FTO (77,206,273), and ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), genetic variants of which impair insulin binding to its receptor in muscle and brain, hence leading to fat deposition (207).

The shape of genetic association studies for type 2 diabetes is set to be transformed in the next few years, with the advent of truly genome-wide association scans. The availability of array-based platforms that will allow the performance of massive parallel genotyping (between 250,000 and 1 million SNPs per assay), combined with the information provided by the International HapMap Consortium, will provide powerful means for a global view of genetic associations in type 2 diabetes (208). Indeed, through the simultaneous analysis of thousands of genetic variants (SNPs) in large diabetes patient cohorts, genome-wide association studies have recently identified the solute carrier family 30 member 8 (SLC30A8), the insulin degrading enzyme (IDE), and hematopoetically expressed homeodomain HHEX (HHEX/IDE) genes, as well as the cyclin-dependent kinase 5 (CDK5) regulatory subunit associated protein-1-like 1 (CDKAL1) melatonin receptor 1B (MTNR1B) (274), the insulin-like growth factor 2 mRNA binding protein (IGF2BP2), and the cyclin-dependent kinase inhibitor 2A ( CDKN2A) genes as type 2 diabetes susceptibility genes (204,206, 209, 210). However, as these loci explain a small proportion of the observed familial cases of the disease, it is expected that additional loci will be revealed in the near future by further systematic screens (211).

Our understanding of the molecular pathways involved in the pathogenesis of the disease could also be enhanced by the utilization of novel technologies. For example, the microarray technology has been used to identify differential mRNA expression patterns in muscle tissue of type 2 diabetes patients and normal controls (212) . The application of metabolomics, which is defined as the measurement of all metabolites present within a cell, tissue, or organism following genetic medication or physiological stimulus, will also contribute valuable insights into the understanding of the pathophysiology of the disease as it provides the potential of globally profiling the metabolome of an organism (213,214). Although few studies of metabolomics have focused on diabetes, a recent application of the technology to type 2 diabetes has identified characteristic alterations in the plasma phospholipids profile, therefore enabling the identification of patients from control individuals (215,216).

3.1. Evidence for Genetic Overlap between Type 1 and Type 2 Diabetes

Even though, as described above, type 1 and type 2 diabetes represent two different disease entities, the clinical and etiological distinction between them is becoming more difficult as there is increasing evidence of a significant overlap between the two disease states. Clinical studies have reported that even within the same family both type 1 and type 2 diabetes may co-occur and patients with such double genetic predisposition have intermediate phenotype (259). As an example of common genetic predisposition, a variable number of tandem repeats polymorphism in the insulin gene promoter region has been associated with both type 1 and type 2 diabetes (259).

The “accelerator hypothesis” suggests that both type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic background (260) . According to this hypothesis, type 1 and type 2 diabetes are one and the same entity, distinguished only by the rate of β cell loss. Instead of overlap between the two types of diabetes, the hypothesis envisages overlay between the two types, with one disease representing a subset of the other.

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