**2. Heritability of MetS and IR**

The pieces of evidence for the heritability and co-occurrence of the metabolic traits have been revealed through early familial and twin genetic studies. The heritability of MetS, as defined by NCEP:ATPIII (National Cholesterol Education Program Adult Treatment Panel III) criteria, was estimated to be 24% (*p* = 0.006) in the Northern Manhattan Family Study, which was conducted in 803 subjects from 89 Caribbean-Hispanic families [9]. Each component of MetS has also an important genetic basis. The heritability was estimated at 46% for waist circumference (WC), 24% for fasting glucose, 60% for HDL-cholesterol, 47% for triacylglycerol (TAG), and 16% for systolic and 21% for diastolic blood pressure (BP). In the Linosa Study including 293 Caucasian native subjects from 51 families (123 parents and 170 offsprings), the heritability of MetS, as defined by NCEP:ATPIII, was estimated to be 27% (*p* = 0.0012) [12]. Among its components, the heritability for blood glucose and high-density lipoprotein (HDL)-cholesterol was 10% and 54%, respectively. The highest heritability was observed in the clustering of central obesity, hypertriglycemia, and low HDL-cholesterolemia (31%, *p* < 0.001). In an early study including 2508 adult male twin pairs, the accordance for the clustering of hypertension, diabetes, and obesity in the same individuals was 31.6% in monozygotic pairs and 6.3% in dizygotic pairs [13]. These early pieces of evidence have spurred many studies to find genetic determinants of MetS.

Although common genetic variants related to IR have been identified, these variants are known to make up only 25–44% of the heritability of IR [14–16]. For this reason, it is necessary to find low-frequency and rare genetic variations that affect the heritability of MetS and IR.

## **3. Genetic variants of MetS and IR**

Significant progress has been made over the past decade to identify the genetic risk factors associated with the various traits of MetS. Although the complexity of MetS makes the identification of a genetic component of the disorder difficult, pieces of evidence for genetic determinants of MetS have been revealed through the linkage analysis approach, candidate gene association studies, GWAS, epigenetic studies, microRNAs, long-non-coding RNAs, system biological studies, and more recently NGS and whole-exome sequencing.

#### **3.1 Linkage analysis approach**

Many chromosomes and locus associated with MetS or its components or a combination of some of its components have been identified through linkage

**213**

MetS [24].

*Genetic Diversity of Insulin Resistance and Metabolic Syndrome*

analysis. This approach has identified candidate quantitative trait loci (QTL). In 2209 subjects from 507 Caucasian families, a QTL associated with body mass index (BMI), WC, and fasting plasma insulin on chromosome 3q27 was identified, which includes genes such as the solute carrier family 2 of the facilitated glucose

In a study including four ethnic groups (Caucasian, Mexican-American, African-American, and Japanese-American), evidence of linkage of MetS traits (weight/waist, lipid factor, and BP) was identified, where there is a strong linkage on chromosome 2q12.1-2q12 for Caucasian subjects and 3q26.1-3q29 for Mexican-

and 1082 subjects from 256 sibships, where a genomic region on chromosome 2 included a pleiotropic locus contributing to the clustering of multiple metabolic syndrome (MMS)-related phenotypes (BMI, waist-to-hip ratio (WHR), subscapular skinfold, TAG, HDL-cholesterol, homeostasis model assessment (HOMA) index,

plasminogen activator inhibitor-1-antigen, and serum uric acid) [20].

through a genome-scan and linkage approach was revealed [22].

Genetic data were obtained for 2467 subjects from 387 three-generation families

In a study including 250 German families, a genome-wide linkage scan for T2D supports the existence of MetS locus on chromosome 1p36.13 and T2D locus on

In a study with 715 individuals in 39 low-income Mexican American families, strong evidence of a major locus near markers *D1S1597* and *D1S407* on chromosome 1p36.21 that influences variation in symptomatic or clinical gallbladder disease

Candidate gene association studies identify and investigate many candidate genes that regulate biological processes related to MetS. Analysis of the mutation burden of candidate genes is among the first methods used to uncover MetS genes. Especially, the association of MetS and single nucleotide polymorphisms (SNPs) in

An association with MetS for 8 SNPs that are mostly in 25 genes involved in lipid metabolism was revealed in 88 studies with 4000 subjects. In these studies, the minor allele of C56G (*APOA5*), T1131C (*APOA5*), rs9939609 (*FTO*), C455T (*APOC3*), rs7903146 (*TCF7L2*), C482T (*APOC3*), and 174G > C (*IL6*) were more prevalent in subjects with MetS but the minor allele of Taq-1B (*CETP*) was less

The association of *HSD11B1* variants and *HSD11B1* expression in abdominal adipose tissue with T2D, MetS, and obesity was identified in 802 studies. Especially, a polymorphic variant was identified to be related to T2D in a study including Pima Indians, and an association between MetS with another polymorphic variant at the *HSD11B1* gene was identified in an Indian study. However, most studies did not find an association between *HSD11B1* polymorphic variants and T2D, MetS, and obesity, suggesting that the variants may play a minor role to develop obesity, T2D, and

A meta-analysis study including 25 reports revealed an association of *ADIPOQ* rs2241766 and rs266729 polymorphisms with MetS in the Chinese population,

In a study including 456 Caucasian (white) and 217 African-American (black) subjects from 204 families, evidence of linkage for increased body fat, abdominal visceral fat, TAG, fasting glucose, fasting plasma insulin, blood pressure, and decreased HDL-cholesterol was identified on chromosome 10p11.2 and 19q13.4 and 10q13.4 in white [18]. In black subjects, the linkage was identified on chromosome

*DOI: http://dx.doi.org/10.5772/intechopen.93906*

transporter (*GLUT2*) [17].

American subjects [19].

chromosome 16p12.2 [21].

prevalent in those [23].

**3.2 Candidate gene association studies**

related genes has been examined in many studies.

1p34.1 [18].

*Genetic Diversity of Insulin Resistance and Metabolic Syndrome DOI: http://dx.doi.org/10.5772/intechopen.93906*

*Genetic Variation*

to these metabolic disorder-associated traits.

spurred many studies to find genetic determinants of MetS.

affect the heritability of MetS and IR.

**3. Genetic variants of MetS and IR**

recently NGS and whole-exome sequencing.

**3.1 Linkage analysis approach**

terms of genetic diversity.

**2. Heritability of MetS and IR**

Although environmental factors such as lifestyle and socioeconomic status contribute to the development of IR and MetS, both IR and MetS are also being determined by genetic factors, as strongly evidenced by early familial genetic studies [9–11]. Based on these studies, advanced genetic analysis technologies such as genome-wide association studies (GWAS) and next-generation sequencing (NGS) are extensively being used to identify both common and rare genetic variants related

This chapter is to present an overview of genetic variants involved in the pathogeneses of MetS and IR and to review the relevance between IR and MetS clusters in

The pieces of evidence for the heritability and co-occurrence of the metabolic traits have been revealed through early familial and twin genetic studies. The heritability of MetS, as defined by NCEP:ATPIII (National Cholesterol Education Program Adult Treatment Panel III) criteria, was estimated to be 24% (*p* = 0.006) in the Northern Manhattan Family Study, which was conducted in 803 subjects from 89 Caribbean-Hispanic families [9]. Each component of MetS has also an important genetic basis. The heritability was estimated at 46% for waist circumference (WC), 24% for fasting glucose, 60% for HDL-cholesterol, 47% for triacylglycerol (TAG), and 16% for systolic and 21% for diastolic blood pressure (BP). In the Linosa Study including 293 Caucasian native subjects from 51 families (123 parents and 170 offsprings), the heritability of MetS, as defined by NCEP:ATPIII, was estimated to be 27% (*p* = 0.0012) [12]. Among its components, the heritability for blood glucose and high-density lipoprotein (HDL)-cholesterol was 10% and 54%, respectively. The highest heritability was observed in the clustering of central obesity, hypertriglycemia, and low HDL-cholesterolemia (31%, *p* < 0.001). In an early study including 2508 adult male twin pairs, the accordance for the clustering of hypertension, diabetes, and obesity in the same individuals was 31.6% in monozygotic pairs and 6.3% in dizygotic pairs [13]. These early pieces of evidence have

Although common genetic variants related to IR have been identified, these variants are known to make up only 25–44% of the heritability of IR [14–16]. For this reason, it is necessary to find low-frequency and rare genetic variations that

Significant progress has been made over the past decade to identify the genetic risk factors associated with the various traits of MetS. Although the complexity of MetS makes the identification of a genetic component of the disorder difficult, pieces of evidence for genetic determinants of MetS have been revealed through the linkage analysis approach, candidate gene association studies, GWAS, epigenetic studies, microRNAs, long-non-coding RNAs, system biological studies, and more

Many chromosomes and locus associated with MetS or its components or a combination of some of its components have been identified through linkage

**212**

analysis. This approach has identified candidate quantitative trait loci (QTL). In 2209 subjects from 507 Caucasian families, a QTL associated with body mass index (BMI), WC, and fasting plasma insulin on chromosome 3q27 was identified, which includes genes such as the solute carrier family 2 of the facilitated glucose transporter (*GLUT2*) [17].

In a study including 456 Caucasian (white) and 217 African-American (black) subjects from 204 families, evidence of linkage for increased body fat, abdominal visceral fat, TAG, fasting glucose, fasting plasma insulin, blood pressure, and decreased HDL-cholesterol was identified on chromosome 10p11.2 and 19q13.4 and 10q13.4 in white [18]. In black subjects, the linkage was identified on chromosome 1p34.1 [18].

In a study including four ethnic groups (Caucasian, Mexican-American, African-American, and Japanese-American), evidence of linkage of MetS traits (weight/waist, lipid factor, and BP) was identified, where there is a strong linkage on chromosome 2q12.1-2q12 for Caucasian subjects and 3q26.1-3q29 for Mexican-American subjects [19].

Genetic data were obtained for 2467 subjects from 387 three-generation families and 1082 subjects from 256 sibships, where a genomic region on chromosome 2 included a pleiotropic locus contributing to the clustering of multiple metabolic syndrome (MMS)-related phenotypes (BMI, waist-to-hip ratio (WHR), subscapular skinfold, TAG, HDL-cholesterol, homeostasis model assessment (HOMA) index, plasminogen activator inhibitor-1-antigen, and serum uric acid) [20].

In a study including 250 German families, a genome-wide linkage scan for T2D supports the existence of MetS locus on chromosome 1p36.13 and T2D locus on chromosome 16p12.2 [21].

In a study with 715 individuals in 39 low-income Mexican American families, strong evidence of a major locus near markers *D1S1597* and *D1S407* on chromosome 1p36.21 that influences variation in symptomatic or clinical gallbladder disease through a genome-scan and linkage approach was revealed [22].

#### **3.2 Candidate gene association studies**

Candidate gene association studies identify and investigate many candidate genes that regulate biological processes related to MetS. Analysis of the mutation burden of candidate genes is among the first methods used to uncover MetS genes. Especially, the association of MetS and single nucleotide polymorphisms (SNPs) in related genes has been examined in many studies.

An association with MetS for 8 SNPs that are mostly in 25 genes involved in lipid metabolism was revealed in 88 studies with 4000 subjects. In these studies, the minor allele of C56G (*APOA5*), T1131C (*APOA5*), rs9939609 (*FTO*), C455T (*APOC3*), rs7903146 (*TCF7L2*), C482T (*APOC3*), and 174G > C (*IL6*) were more prevalent in subjects with MetS but the minor allele of Taq-1B (*CETP*) was less prevalent in those [23].

The association of *HSD11B1* variants and *HSD11B1* expression in abdominal adipose tissue with T2D, MetS, and obesity was identified in 802 studies. Especially, a polymorphic variant was identified to be related to T2D in a study including Pima Indians, and an association between MetS with another polymorphic variant at the *HSD11B1* gene was identified in an Indian study. However, most studies did not find an association between *HSD11B1* polymorphic variants and T2D, MetS, and obesity, suggesting that the variants may play a minor role to develop obesity, T2D, and MetS [24].

A meta-analysis study including 25 reports revealed an association of *ADIPOQ* rs2241766 and rs266729 polymorphisms with MetS in the Chinese population,

where the G allele of rs2241766 increased the risk of MetS but no relevance to rs266729 was found [25].

In a study including 442 adults with MetS, it was revealed that *APOE* genotype affected IR, apolipoprotein (apo) CII, and CIII depending on plasma fatty acid (FA) levels in MetS. Elevated n-3 polyunsaturated FA (PUFA) was related to lower concentration of apo CIII in *E2* carriers and elevated C16:0 was related to IR in *E4* carriers. Decreased long-chain n-3 PUFA was associated with reduced apo CII level in *E2* carriers, after FA intervention. These results suggest that subjects with MetS may benefit from personalized dietary interventions based on *APOE* genotype [26].

A meta- and gene-based analysis including 18 studies was carried out to investigate the association of fat mass and obesity-related *FTO* gene polymorphisms with MetS, suggesting that FTO is strongly related to MetS (*p* < 10−5) [27].

BALB/c mice are known to be resistant to a high-fat diet (HFD)-induced obesity. A recent study demonstrated that *Nod2−*/*−*BALB/c mice developed HFD-dependent obesity and risk factors of MetS such as hyperglycemia and hyperlipidemia. Interestingly, *Nod2−*/*−* HFD mice showed changes in the composition of gut flora and also delivered sensitivity to hyperglycemia, steatosis, and weight gain to wild type germ-free mice. Therefore, these results suggest that not only *Nod2* plays a novel role in obesity but also that *Nod2* and *Nod2*-regulated gut flora protect BALB/c mice from diet-induced obesity and metabolic disorders [28].

More recently, a multiple-genotype and multiple-phenotype analysis of a genebased SNP set has been performed to identify new susceptible variants associated with MetS in 10,049 Korean individuals [29]. In this study, 27 SNP pairs were associated with MetS in the discovery stage and also replicated. Of these SNPs, 3 SNP pairs in each SIDT2, UBASH3B, and CUX2 gene were significant in the multiple-SNP and multiple-phenotype analysis rather than in the single-SNP and multiple-phenotype analysis. Especially, an association of MetS with an intronic SNP pair, rs7107152 (*p* = 3.89 × 10−14) and rs1242229 (*p* = 3.64 × 10−13), in *SIDT2* gene at 11q23.3 was found. These 2 SNPs are also associated with the expression of *SIDT2* and *TAGLN* that promote insulin secretion and lipid metabolism, respectively. These results suggest the usefulness of the multiple-genotype and multiplephenotype analysis platform to identify new genetic loci in complex metabolic disorders such as MetS.

Although candidate genetic association studies have reported many genetic variations associated with MetS, often these results have not been replicated in other populations and been identified through GWAS. These examples include polymorphisms in or near genes encoding GAD2, ENPP1, and SCL6A14. Moreover, most of the identified genes underlie only one MetS trait. Few exceptions contain mutations in *ADIPOQ* related to hypertension, T2D, and dyslipidemia. Other examples contain mutations in *FOXC2*, *SREBP1*, *NR3C1*, and *GNB3* genes.

#### **3.3 GWAS**

GWA studies are an approach used to analyze an association of SNPs in subjects with MetS or IR and to date, being carried out by many researchers.

#### *3.3.1 Genetic diversity of MetS*

Over the past 10 years, GWAS have identified many genetic variants associated with each trait of MetS. Many genetic loci associated with lipid levels were discovered and refined by GWAS which identified 157 loci related to lipid levels at *p* < 5 x 10−8, including 62 loci not previously related to lipid levels [30]. Among the loci, 39 loci were associated with TAG levels and 71 with HDL-cholesterol.

**215**

*Genetic Diversity of Insulin Resistance and Metabolic Syndrome*

Several loci associated with each component of MetS have pleiotropic effects on

A GWA meta-analysis including 76,150 subjects showed that the rs2943634 variant near *IRS1* was associated with an elevated visceral to subcutaneous fat ratio, IR, dyslipidemia (higher TAG and lower HDL-cholesterol), risk of T2D, and reduced adiponectin levels [31]. Genetic variants in the *GCKR* gene were linked to fasting glucose levels [32], TAG [33], and non-alcoholic fatty liver disease [34]. Variants for obesity in/near *FTO* and *MC4R* genes were associated with specific measures of adiposity such as WC [35], HDL-cholesterol levels [30], IR [36, 37], and risk of T2D [35]. Variants in the *GRB14* gene were also linked to BMI-adjusted

In a GWAS comparing T2D subjects (n = 1924) and control (n = 2938) for autosomal SNPs (n = 490,032), SNPs in *FTO* gene region on chromosome 16 were identified to be strongly associated with T2D (e.g., rs9939609, OR = 1.27, *p* = 5 × 10−8). This strong association was furthermore reproduced by analyzing SNP rs9939609 in T2D subjects (n = 3757) and controls (n = 5346) (OR = 1.15, *p* = 9 × 10−6) [35]. However, some of these variants were also associated with MetS, suggesting that genes such as *FTO*, *MC4R*, and *IRS1* play important roles in the progression of MetS [40]. Especially, among several obesity-related loci found to be related to MetS-related traits in the GWAS studies, *FTO* and *MC4R* genes are considered to be the strongest candidates for body weight control, and *IRS1* is known to have an important effect on IR. These results may provide valuable information to understand the role of genetic

GWA studies of MetS as a whole or a combination of its traits have also identified a number of both common and rare genetic variants. A GWA study was conducted to identify common genetic variants of MetS and its related components in 4560 Indian Asian men with a high prevalence of these conditions. In this study, no genetic variation showed an association with MetS as a whole. However, several variations were related to single components. Especially, 2 SNPs near *CETP*, 2 at 8p21.3 near *LPL*, 2 at 11q12.2 near *FADS1* and *FADS2*, and 1 at 21q22.3 near *FLJ41733* associated with HDL-cholesterol (*p* < 10−6), and 1 SNP near *TCF7L2* associated with

A study by the STAMPEDE Consortium included 13 independent studies, comprising a total of 22,161 subjects of European ancestry, was conducted to find genetic determinants contributing to the correlated architecture of MetS traits, using MetS as a whole or pairs of its components as phenotypes [42]. In this study, the 5 SNPs in *LPL*, *APOA5* cluster (*ZNF259*, *BUD13*, and *APOA5*), and *CETP* genes were found to be associated with MetS. Especially, a total of 27 genetic variants in or near 16 genes were associated with bivariate combinations of 5 MetS traits, including variants in *LIPC* (chromosome 15q21-q23) associated with HDL-cholesterol-fasting glucose (rs2043085, *p* = 1.3 x 10−8) and with WC-HDLcholesterol (rs10468017, *p* = 5.5 x 10−8), *ABCB11* (chromosome 2q24) associated with HDL-cholesterol-fasting glucose (rs569805, p = 8.5 x 10−8) and with HDL-cholesterol-TAG (rs2954026, *p* = 7.9 x 10−9), *TRIB1* (chromosome 8q24.13) associated with TAG-BP (rs2954033, *p* = 8.5 x 10−9), *TFAP2B* (chromosome 6p12) associated with WC-fasting glucose (rs2206277, *p* = 1.3 x 10−7), *LOC100128354* (chromosome 11q21) and *MTNR1B* associated with BP-fasting glucose (rs1387153, *p* = 8.1 x 10−9), HDL-cholesterol-fasting glucose (rs1387153, p = 2.4 x 10−9), and TAG-fasting glucose (rs10830956, *p* = 4.8 x 10−11), *LOC100129500* (chromosome 19q13.2) associated with HDL-cholesterol-TAG (rs439401, *p* = 1.0 x 10−8), and LOC100129150 variants with HDL-cholesterol-TAG (rs9987289, *p* = 1.1 x 10−8) and HDL-cholesterol-WC (rs9987289, *p* = 3.7 x 10−8) [42]. These common genetic

variations can partly explain the covariation in the MetS traits.

*DOI: http://dx.doi.org/10.5772/intechopen.93906*

WHR [38], T2D [39], and fasting insulin levels.

control of adiposity and IR in the development of MetS.

T2D (*p* < 10−6) were identified [41].

two or more traits related to MetS.

#### *Genetic Diversity of Insulin Resistance and Metabolic Syndrome DOI: http://dx.doi.org/10.5772/intechopen.93906*

*Genetic Variation*

rs266729 was found [25].

disorders such as MetS.

*3.3.1 Genetic diversity of MetS*

where the G allele of rs2241766 increased the risk of MetS but no relevance to

MetS, suggesting that FTO is strongly related to MetS (*p* < 10−5) [27].

mice from diet-induced obesity and metabolic disorders [28].

obesity and risk factors of MetS such as hyperglycemia and hyperlipidemia. Interestingly, *Nod2−*/*−* HFD mice showed changes in the composition of gut flora and also delivered sensitivity to hyperglycemia, steatosis, and weight gain to wild type germ-free mice. Therefore, these results suggest that not only *Nod2* plays a novel role in obesity but also that *Nod2* and *Nod2*-regulated gut flora protect BALB/c

In a study including 442 adults with MetS, it was revealed that *APOE* genotype affected IR, apolipoprotein (apo) CII, and CIII depending on plasma fatty acid (FA) levels in MetS. Elevated n-3 polyunsaturated FA (PUFA) was related to lower concentration of apo CIII in *E2* carriers and elevated C16:0 was related to IR in *E4* carriers. Decreased long-chain n-3 PUFA was associated with reduced apo CII level in *E2* carriers, after FA intervention. These results suggest that subjects with MetS may benefit from personalized dietary interventions based on *APOE* genotype [26]. A meta- and gene-based analysis including 18 studies was carried out to investigate the association of fat mass and obesity-related *FTO* gene polymorphisms with

BALB/c mice are known to be resistant to a high-fat diet (HFD)-induced obesity. A recent study demonstrated that *Nod2−*/*−*BALB/c mice developed HFD-dependent

More recently, a multiple-genotype and multiple-phenotype analysis of a genebased SNP set has been performed to identify new susceptible variants associated with MetS in 10,049 Korean individuals [29]. In this study, 27 SNP pairs were associated with MetS in the discovery stage and also replicated. Of these SNPs, 3 SNP pairs in each SIDT2, UBASH3B, and CUX2 gene were significant in the multiple-SNP and multiple-phenotype analysis rather than in the single-SNP and multiple-phenotype analysis. Especially, an association of MetS with an intronic SNP pair, rs7107152 (*p* = 3.89 × 10−14) and rs1242229 (*p* = 3.64 × 10−13), in *SIDT2* gene at 11q23.3 was found. These 2 SNPs are also associated with the expression of *SIDT2* and *TAGLN* that promote insulin secretion and lipid metabolism, respectively. These results suggest the usefulness of the multiple-genotype and multiplephenotype analysis platform to identify new genetic loci in complex metabolic

Although candidate genetic association studies have reported many genetic variations associated with MetS, often these results have not been replicated in other populations and been identified through GWAS. These examples include polymorphisms in or near genes encoding GAD2, ENPP1, and SCL6A14. Moreover, most of the identified genes underlie only one MetS trait. Few exceptions contain mutations in *ADIPOQ* related to hypertension, T2D, and dyslipidemia. Other examples contain mutations in *FOXC2*, *SREBP1*, *NR3C1*, and *GNB3* genes.

GWA studies are an approach used to analyze an association of SNPs in subjects

Over the past 10 years, GWAS have identified many genetic variants associated with each trait of MetS. Many genetic loci associated with lipid levels were discovered and refined by GWAS which identified 157 loci related to lipid levels at *p* < 5 x 10−8, including 62 loci not previously related to lipid levels [30]. Among the loci, 39 loci were associated with TAG levels and 71 with HDL-cholesterol.

with MetS or IR and to date, being carried out by many researchers.

**214**

**3.3 GWAS**

Several loci associated with each component of MetS have pleiotropic effects on two or more traits related to MetS.

A GWA meta-analysis including 76,150 subjects showed that the rs2943634 variant near *IRS1* was associated with an elevated visceral to subcutaneous fat ratio, IR, dyslipidemia (higher TAG and lower HDL-cholesterol), risk of T2D, and reduced adiponectin levels [31]. Genetic variants in the *GCKR* gene were linked to fasting glucose levels [32], TAG [33], and non-alcoholic fatty liver disease [34]. Variants for obesity in/near *FTO* and *MC4R* genes were associated with specific measures of adiposity such as WC [35], HDL-cholesterol levels [30], IR [36, 37], and risk of T2D [35]. Variants in the *GRB14* gene were also linked to BMI-adjusted WHR [38], T2D [39], and fasting insulin levels.

In a GWAS comparing T2D subjects (n = 1924) and control (n = 2938) for autosomal SNPs (n = 490,032), SNPs in *FTO* gene region on chromosome 16 were identified to be strongly associated with T2D (e.g., rs9939609, OR = 1.27, *p* = 5 × 10−8). This strong association was furthermore reproduced by analyzing SNP rs9939609 in T2D subjects (n = 3757) and controls (n = 5346) (OR = 1.15, *p* = 9 × 10−6) [35]. However, some of these variants were also associated with MetS, suggesting that genes such as *FTO*, *MC4R*, and *IRS1* play important roles in the progression of MetS [40]. Especially, among several obesity-related loci found to be related to MetS-related traits in the GWAS studies, *FTO* and *MC4R* genes are considered to be the strongest candidates for body weight control, and *IRS1* is known to have an important effect on IR. These results may provide valuable information to understand the role of genetic control of adiposity and IR in the development of MetS.

GWA studies of MetS as a whole or a combination of its traits have also identified a number of both common and rare genetic variants. A GWA study was conducted to identify common genetic variants of MetS and its related components in 4560 Indian Asian men with a high prevalence of these conditions. In this study, no genetic variation showed an association with MetS as a whole. However, several variations were related to single components. Especially, 2 SNPs near *CETP*, 2 at 8p21.3 near *LPL*, 2 at 11q12.2 near *FADS1* and *FADS2*, and 1 at 21q22.3 near *FLJ41733* associated with HDL-cholesterol (*p* < 10−6), and 1 SNP near *TCF7L2* associated with T2D (*p* < 10−6) were identified [41].

A study by the STAMPEDE Consortium included 13 independent studies, comprising a total of 22,161 subjects of European ancestry, was conducted to find genetic determinants contributing to the correlated architecture of MetS traits, using MetS as a whole or pairs of its components as phenotypes [42]. In this study, the 5 SNPs in *LPL*, *APOA5* cluster (*ZNF259*, *BUD13*, and *APOA5*), and *CETP* genes were found to be associated with MetS. Especially, a total of 27 genetic variants in or near 16 genes were associated with bivariate combinations of 5 MetS traits, including variants in *LIPC* (chromosome 15q21-q23) associated with HDL-cholesterol-fasting glucose (rs2043085, *p* = 1.3 x 10−8) and with WC-HDLcholesterol (rs10468017, *p* = 5.5 x 10−8), *ABCB11* (chromosome 2q24) associated with HDL-cholesterol-fasting glucose (rs569805, p = 8.5 x 10−8) and with HDL-cholesterol-TAG (rs2954026, *p* = 7.9 x 10−9), *TRIB1* (chromosome 8q24.13) associated with TAG-BP (rs2954033, *p* = 8.5 x 10−9), *TFAP2B* (chromosome 6p12) associated with WC-fasting glucose (rs2206277, *p* = 1.3 x 10−7), *LOC100128354* (chromosome 11q21) and *MTNR1B* associated with BP-fasting glucose (rs1387153, *p* = 8.1 x 10−9), HDL-cholesterol-fasting glucose (rs1387153, p = 2.4 x 10−9), and TAG-fasting glucose (rs10830956, *p* = 4.8 x 10−11), *LOC100129500* (chromosome 19q13.2) associated with HDL-cholesterol-TAG (rs439401, *p* = 1.0 x 10−8), and LOC100129150 variants with HDL-cholesterol-TAG (rs9987289, *p* = 1.1 x 10−8) and HDL-cholesterol-WC (rs9987289, *p* = 3.7 x 10−8) [42]. These common genetic variations can partly explain the covariation in the MetS traits.

In a study for susceptibility loci associated with MetS and its traits was conducted in four Finnish cohorts consisting of 2637 MetS cases and 7927 controls. One genetic variant (rs964184) in A *APOA1/C3/A4/A5* gene cluster region on chromosome 11, known as lipid locus was found to be associated with MetS in all 4 study samples (*p* = 7.23 × 10−9 in meta-analysis) and significantly associated with several very lowdensity lipoprotein (VLDL), TAG, and HDL metabolites (*p* = 0.024–1.88 × 10−5). Several genetic variants in or near 4 known loci related to lipids (LPL, CEPT, APOA1/C3/A4/A5, and APOB) were strongly associated with TAG/HDL/WC factors [43], but none was associated with 2 or more uncorrelated MetS traits. A polygenetic risk score (PRS), which was calculated as the number of alleles in loci associated with individual MetS traits, was significantly associated with MetS traits. These results suggest that genes associated with lipid metabolism pathways have crucial roles in the development of MetS. However, in this study, little evidence for pleiotropy associating obesity and dyslipidemia with the other MetS traits (hyperglycemia and hypertension) was found.

Genetic loci associated with the clustering of 6 MetS-related phenotypes (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, prothrombotic state, central obesity, and elevated plasma glucose) including 19 quantitative traits were identified by GWAS in 19,486 European American and 6287 African American Candidate Gene Association Resource Consortium participants [44]. In this study, 606 significant SNPs in and near 19 loci (*p* = 2.13 x 10−7) were identified in European Americans. Many of these loci were associated with at least one MetS-related trait domain and consistent with results in African Americans. Three new pleiotropic loci in or near *APOC1*, *BRAP*, and *PLCG1*, which were associated with multiple phenotype domains were identified. Several loci previously identified by GWAS for each trait of MetS, including *LPL*, *ABCA1*, and *GCKR*, were also associated with at least 2 trait domains. These results support the presence of genetic variants with pleiotropic effects on adiposity, inflammation, glucose regulation, dyslipidemia, vascular dysfunction, and thrombosis. Such loci could apply to uncover metabolic dysregulation and identify targets for early intervention.

#### *3.3.2 Genetic diversity of IR*

To date, many of the loci related to risks of developing IR have been identified and found to be associated with measures such as insulin sensitivity and secretion.

In an early meta-analysis, genetic variants related to IR were identified in 21 cohorts consisting of a non-diabetic group, which includes 46,186 subjects with measures of fasting glucose and 38,238 subjects with measures of fasting glucose and HOMA-IR. In additional 76,558 subjects, 25 SNPs were followed up with this approach, identifying 16 loci related to fasting glucose and 2 loci related to fasting insulin. In this study, several loci near *GCKR* including a new locus near *IGF1* were found to be associated with IR [32]. These results were replicated in a further 14 cohorts, which included 29,084 non-diabetic subjects with measures of fasting proinsulin, insulin secretion, and sensitivity [45]. Association of 37 risk loci for T2D with measures of insulin secretion, sensitivity, and processing and clearance was examined in 58,614 non-diabetic subjects and 17,327 subjects with measures of glycemic traits, revealing that the risk loci were grouped into 5 major categories including one cluster with 4 loci (*PPARG*, *KLF14*, *IRS1*, and *GCKR*) associated with IR [46].

A joint meta-analysis (JMA) approach has been developed to identify genetic variants associated with either fasting glucose and/or fasting insulin. This approach identified 6 loci that include 5 new variants associated with levels of fasting insulin (*IRS1*, *COBLL1-GRB14*, *PPP1R3B*, *PDGFC*, *UHRF1BP1*, and *LYPLAL1*) [47].

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*Genetic Diversity of Insulin Resistance and Metabolic Syndrome*

A large-scale meta-analysis including 133,010 subjects identified 17 loci significantly associated with fasting insulin. These loci included genes associated with other metabolic traits (*FTO*, *TCF7L2*, *PPARG*, ARL15, *RSPO3*, and *ANKRD55- MAP3K1*) and newly identified loci (*YSK4*, *FAM13A*, *TET2*, *PEPD*, and *HIP1*) [48]. In 2 further studies, these loci were used to make an IR PRS identify the relationship between variants associated with fasting insulin and the risk of each individual developing IR and T2D [49, 50]. The 2 studies identified that the IR GRSs were associated with decreased insulin sensitivity and lower BMI. In one of these 2 studies, a PRS was generated from 10 genetic loci that were related to lower HDL-cholesterol and higher TAG (*PPARG*, *IRS1*, *GRB14*, *PEPD*, *FAM13A1*, *PDGFC*, *LYPLAL1*, *RSPO3*, *ARL15*, and *ANKRD55-MAP3K1*) [49]. In the other study, 19 loci were used to generate their IR PGS and 11 risk variants were identified to be related to increased TAG and lower HDL-cholesterol along with a lower BMI [50]. In these studies, IR PRSs were used to highlight that IR can develop without obesity and

IRS1 is a signaling adapter protein that is encoded by the *IRS1* gene in humans

and a key factor of the insulin signaling pathway initiating the activation of phosphoinositide 3-kinase (PI3K) in response to insulin. The C allele at the SNP (rs2943641) near the *IRS1* gene was found to be associated with IR and hyperinsulinemia in a European population. Through functional studies, the risk allele was found to be associated with lower levels of basal IRS1 protein and decreased PI3K activity during insulin infusion, indicating a causative role for the genetic variant on risk of IR [51]. The SNP (rs2943650) near *IRS1* was also associated with lower HDL-cholesterol, elevated TAG, IR, and lower body fat percentage [31]. Significant associations of the variants in *FTO* with fasting insulin and insulin sensitivity were identified [37]. The risk variant in or near *TCF7L2* was found to be associated with both impaired β-cell function and IR [52]. A variant in *NAT2* was also found to be associated with a measure of insulin sensitivity in four European cohorts of 2764 non-diabetic individuals [53], supporting a role for *NAT2* in insulin sensitivity. In this study, a variant of *NAT2* was found to be strongly associated with reduced insulin sensitivity that was independent of BMI. The A allele at the SNP (rs1208) was significantly associated with IR-related traits, including increased fasting glucose, total cholesterol and LDL-cholesterol, hemoglobin A1C (HbA1c), TAG, and coronary artery disease (CHD). IGF1 is functionally similar to insulin and controls growth and development. Lower levels of IGF1 were found to be associated with decreased insulin sensitivity [54], and the SNP (rs35767) in the *IGF1* gene suggested

that the G allele has lower levels of IGF-1 compared to the A allele [55].

T2D, CHD, and IR as measured by HOMA-IR [58].

In a GWA study of a UK cohort of Indian-Asian and European ancestry, *MC4R* was found to be associated with both IR with measures of HOMA-IR and WC, and with higher frequencies of risk alleles found in the Indian-Asian cohort [36]. In a GWA study of a cohort with Indian ancestry, 2 loci near *TMEM163* were found to be associated with both reduced plasma insulin and HOMA-IR [56]. In a GWA study of an African-American cohort, the SNP (rs7077836) near *TCERG1L* and the SNP (rs17046216) in *SC4MOL* were found to be associated with both fasting insulin and HOMA-IR [57]. *ARL15* belongs to a family of intracellular vesicle trafficking, and its exact function remains unknown. However, variants in *ARL15* were found to be associated with decreased levels of adiponectin and risk of

To date, approximately 60 loci related to the risk of IR have been identified through GWAS, and among them, the top 10 IR-related loci have been replicated in 2 GWA studies [48, 59]. They are in and near the noncoding regions of *IRS1* (rs2943645)*, PPARG* (rs17036328), *GRB14* (rs10195252)*, PEPD* (rs731839)*, PDGFC* (rs6822892), *MAP3K1* (rs459193), *ARL15* (rs4865796)*, FAM13A* (rs3822072)*,* 

*DOI: http://dx.doi.org/10.5772/intechopen.93906*

high BMI.

*Genetic Variation*

and hypertension) was found.

*3.3.2 Genetic diversity of IR*

In a study for susceptibility loci associated with MetS and its traits was conducted in four Finnish cohorts consisting of 2637 MetS cases and 7927 controls. One genetic variant (rs964184) in A *APOA1/C3/A4/A5* gene cluster region on chromosome 11, known as lipid locus was found to be associated with MetS in all 4 study samples (*p* = 7.23 × 10−9 in meta-analysis) and significantly associated with several very lowdensity lipoprotein (VLDL), TAG, and HDL metabolites (*p* = 0.024–1.88 × 10−5). Several genetic variants in or near 4 known loci related to lipids (LPL, CEPT, APOA1/C3/A4/A5, and APOB) were strongly associated with TAG/HDL/WC factors [43], but none was associated with 2 or more uncorrelated MetS traits. A polygenetic risk score (PRS), which was calculated as the number of alleles in loci associated with individual MetS traits, was significantly associated with MetS traits. These results suggest that genes associated with lipid metabolism pathways have crucial roles in the development of MetS. However, in this study, little evidence for pleiotropy associating obesity and dyslipidemia with the other MetS traits (hyperglycemia

Genetic loci associated with the clustering of 6 MetS-related phenotypes (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, prothrombotic state, central obesity, and elevated plasma glucose) including 19

quantitative traits were identified by GWAS in 19,486 European American and 6287 African American Candidate Gene Association Resource Consortium participants [44]. In this study, 606 significant SNPs in and near 19 loci (*p* = 2.13 x 10−7) were identified in European Americans. Many of these loci were associated with at least one MetS-related trait domain and consistent with results in African Americans. Three new pleiotropic loci in or near *APOC1*, *BRAP*, and *PLCG1*, which were associated with multiple phenotype domains were identified. Several loci previously identified by GWAS for each trait of MetS, including *LPL*, *ABCA1*, and *GCKR*, were also associated with at least 2 trait domains. These results support the presence of genetic variants with pleiotropic effects on adiposity, inflammation, glucose regulation, dyslipidemia, vascular dysfunction, and thrombosis. Such loci could apply to uncover metabolic dysregulation and identify targets for early intervention.

To date, many of the loci related to risks of developing IR have been identified and found to be associated with measures such as insulin sensitivity and secretion. In an early meta-analysis, genetic variants related to IR were identified in 21 cohorts consisting of a non-diabetic group, which includes 46,186 subjects with measures of fasting glucose and 38,238 subjects with measures of fasting glucose and HOMA-IR. In additional 76,558 subjects, 25 SNPs were followed up with this approach, identifying 16 loci related to fasting glucose and 2 loci related to fasting insulin. In this study, several loci near *GCKR* including a new locus near *IGF1* were found to be associated with IR [32]. These results were replicated in a further 14 cohorts, which included 29,084 non-diabetic subjects with measures of fasting proinsulin, insulin secretion, and sensitivity [45]. Association of 37 risk loci for T2D with measures of insulin secretion, sensitivity, and processing and clearance was examined in 58,614 non-diabetic subjects and 17,327 subjects with measures of glycemic traits, revealing that the risk loci were grouped into 5 major categories including one cluster with 4 loci (*PPARG*, *KLF14*, *IRS1*, and *GCKR*) associated

A joint meta-analysis (JMA) approach has been developed to identify genetic variants associated with either fasting glucose and/or fasting insulin. This approach identified 6 loci that include 5 new variants associated with levels of fasting insulin (*IRS1*, *COBLL1-GRB14*, *PPP1R3B*, *PDGFC*, *UHRF1BP1*, and *LYPLAL1*) [47].

**216**

with IR [46].

A large-scale meta-analysis including 133,010 subjects identified 17 loci significantly associated with fasting insulin. These loci included genes associated with other metabolic traits (*FTO*, *TCF7L2*, *PPARG*, ARL15, *RSPO3*, and *ANKRD55- MAP3K1*) and newly identified loci (*YSK4*, *FAM13A*, *TET2*, *PEPD*, and *HIP1*) [48]. In 2 further studies, these loci were used to make an IR PRS identify the relationship between variants associated with fasting insulin and the risk of each individual developing IR and T2D [49, 50]. The 2 studies identified that the IR GRSs were associated with decreased insulin sensitivity and lower BMI. In one of these 2 studies, a PRS was generated from 10 genetic loci that were related to lower HDL-cholesterol and higher TAG (*PPARG*, *IRS1*, *GRB14*, *PEPD*, *FAM13A1*, *PDGFC*, *LYPLAL1*, *RSPO3*, *ARL15*, and *ANKRD55-MAP3K1*) [49]. In the other study, 19 loci were used to generate their IR PGS and 11 risk variants were identified to be related to increased TAG and lower HDL-cholesterol along with a lower BMI [50]. In these studies, IR PRSs were used to highlight that IR can develop without obesity and high BMI.

IRS1 is a signaling adapter protein that is encoded by the *IRS1* gene in humans and a key factor of the insulin signaling pathway initiating the activation of phosphoinositide 3-kinase (PI3K) in response to insulin. The C allele at the SNP (rs2943641) near the *IRS1* gene was found to be associated with IR and hyperinsulinemia in a European population. Through functional studies, the risk allele was found to be associated with lower levels of basal IRS1 protein and decreased PI3K activity during insulin infusion, indicating a causative role for the genetic variant on risk of IR [51]. The SNP (rs2943650) near *IRS1* was also associated with lower HDL-cholesterol, elevated TAG, IR, and lower body fat percentage [31]. Significant associations of the variants in *FTO* with fasting insulin and insulin sensitivity were identified [37]. The risk variant in or near *TCF7L2* was found to be associated with both impaired β-cell function and IR [52]. A variant in *NAT2* was also found to be associated with a measure of insulin sensitivity in four European cohorts of 2764 non-diabetic individuals [53], supporting a role for *NAT2* in insulin sensitivity. In this study, a variant of *NAT2* was found to be strongly associated with reduced insulin sensitivity that was independent of BMI. The A allele at the SNP (rs1208) was significantly associated with IR-related traits, including increased fasting glucose, total cholesterol and LDL-cholesterol, hemoglobin A1C (HbA1c), TAG, and coronary artery disease (CHD). IGF1 is functionally similar to insulin and controls growth and development. Lower levels of IGF1 were found to be associated with decreased insulin sensitivity [54], and the SNP (rs35767) in the *IGF1* gene suggested that the G allele has lower levels of IGF-1 compared to the A allele [55].

In a GWA study of a UK cohort of Indian-Asian and European ancestry, *MC4R* was found to be associated with both IR with measures of HOMA-IR and WC, and with higher frequencies of risk alleles found in the Indian-Asian cohort [36].

In a GWA study of a cohort with Indian ancestry, 2 loci near *TMEM163* were found to be associated with both reduced plasma insulin and HOMA-IR [56].

In a GWA study of an African-American cohort, the SNP (rs7077836) near *TCERG1L* and the SNP (rs17046216) in *SC4MOL* were found to be associated with both fasting insulin and HOMA-IR [57]. *ARL15* belongs to a family of intracellular vesicle trafficking, and its exact function remains unknown. However, variants in *ARL15* were found to be associated with decreased levels of adiponectin and risk of T2D, CHD, and IR as measured by HOMA-IR [58].

To date, approximately 60 loci related to the risk of IR have been identified through GWAS, and among them, the top 10 IR-related loci have been replicated in 2 GWA studies [48, 59]. They are in and near the noncoding regions of *IRS1* (rs2943645)*, PPARG* (rs17036328), *GRB14* (rs10195252)*, PEPD* (rs731839)*, PDGFC* (rs6822892), *MAP3K1* (rs459193), *ARL15* (rs4865796)*, FAM13A* (rs3822072)*,* 

*RSPO3* (rs2745353) and *LYPLAL1* (rs4846565). The PRS including the risk alleles of the 10 loci was associated with the cardiometabolic phenotypes such as lower BMI, lower body fat percentage, smaller hip circumference, and decreased leg fat mass as well as the risk phenotypes such as higher fasting insulin and higher TAG levels. These results suggest that limited storage capacity of subcutaneous adipose tissue (SAT) and consequently the elevation of ectopic fat deposition may be associated with the genetic link with IR [48, 49].
