A key in the etiology of a cluster of metabolic syndrome such as hyperglycemia, dyslipidemia, and obesity is known for insulin resistance, which is becoming a major global public health problem. Extensive studies have revealed many genetic factors for both insulin resistance and the components of metabolic syndrome. Advanced modern genotyping methods including genome-wide association studies and next-generation sequencing have allowed for the identification of both common and rare genetic variants related to these chronic disease-associated traits. Multiple genotype–phenotype studies are also needed to identify new and accurate genetic biomarkers in these conditions. The purpose of this chapter is to present genetic variants related to the pathogenesis of metabolic syndrome and insulin resistance and is to review the relevance between insulin resistance and metabolic syndrome clusters in terms of genetic diversity.
- metabolic disorders
- genetic variation
- genetic biomarker
- genetic analysis
Metabolic syndrome (MetS), known as syndrome X, Deadly Quartet, or insulin resistance syndrome is characterized by a cluster of metabolic risk factors such as obesity, hypertension, dyslipidemia, and elevated fasting plasma glucose . The metabolic risk factors can result in type 2 diabetes (T2D) and cardiovascular disease (CVD) that are due to both genetic and environmental factors [2, 3]. For these reasons, MetS is becoming a global epidemic. The prevalence of MetS is estimated at 11.9–37.1% in Asia-Pacific region , 11.6–26.3% in Europe , and 22–24% in North America .
One of the primary mediators of MetS is known for insulin resistance (IR), which is a pathological state of improper cellular response to the hormone insulin in insulin-dependent cells such as skeletal muscle and adipose tissue . IR is present in the majority of people with many metabolic disorders such as MetS and T2D. IR plays a crucial role in the pathophysiology of both T2D and CVD  but inversely related to insulin sensitivity in insulin-dependent tissues . Clinical risk factors such as obesity, dyslipidemia, inflammation, hyperinsulinemia, and dysglycemia are also known to affect 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, 10, 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 to these metabolic disorder-associated traits.
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 terms of genetic diversity.
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% (
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, 15, 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 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 (
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 . In black subjects, the linkage was identified on chromosome 1p34.1 .
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 .
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) .
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 .
In a study with 715 individuals in 39 low-income Mexican American families, strong evidence of a major locus near markers
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 (
The association of
A meta-analysis study including 25 reports revealed an association of
In a study including 442 adults with MetS, it was revealed that
A meta- and gene-based analysis including 18 studies was carried out to investigate the association of fat mass and obesity-related
BALB/c mice are known to be resistant to a high-fat diet (HFD)-induced obesity. A recent study demonstrated that
More recently, a multiple-genotype and multiple-phenotype analysis of a gene-based SNP set has been performed to identify new susceptible variants associated with MetS in 10,049 Korean individuals . 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 (
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
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
A GWA meta-analysis including 76,150 subjects showed that the rs2943634 variant near
In a GWAS comparing T2D subjects (n = 1924) and control (n = 2938) for autosomal SNPs (n = 490,032), SNPs in
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
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 . In this study, the 5 SNPs in
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
Genetic loci associated with the clustering of 6 MetS-related phenotypes (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic 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 . In this study, 606 significant SNPs in and near 19 loci (
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
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 (
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 (
IRS1 is a signaling adapter protein that is encoded by the
In a GWA study of a UK cohort of Indian-Asian and European ancestry,
In a GWA study of a cohort with Indian ancestry, 2 loci near
In a GWA study of an African-American cohort, the SNP (rs7077836) near
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
3.4 Low-frequency and rare variants
Whole-genome and exome sequencing approaches as relatively new genetic analysis technologies are being used to pinpoint the effects of minor allele frequencies (MAF ≤ 5%) and rare variants (MAF ≤ 0.5%) on the heritability of metabolic disorders such as MetS and IR.
The genomes of 1092 individuals from 14 populations were analyzed by using both the whole-genome and exome sequencing methods to identify low-frequency and rare genetic variants across 14 populations in the 1000 Genome Project . The reference panels gained from this project can capture up to 98% accessible SNPs at a frequency of 1% in related populations and also enable researchers to analyze common and low-frequency variants in each individual from various populations. The 38 million SNP panels from the 1000 Genomes Project gave near complete coverage of common and low-frequency genetic variation with MAF ≥0.5% across European ancestry populations.
The European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium carried out 22 GWAS to examine associations of genetic variants with WHR, fasting glucose, BMI, and fasting insulin in 87,048 individuals of European ancestry. This study identified two new loci for BMI, and fasting glucose and new lead SNPs at 29 loci including the SNP (rs1260326) near
Whole exome sequencing in a Danish cohort of 1000 individuals with T2D, BMI >27.5 kg/m2, and hypertension and of 1000 controls identified 70,182 SNPs with MAF > 1%. Subsequent exome sequencing was performed in a two-stage follow-up in 15,989 Danes and a further 63,896 Europeans. This study showed associations of two common SNPs in
Although physiological functions of risk variants in
Exome sequencing in an Icelandic population revealed that a low-frequency (1.47%) variant (rs76895963) in
In 2733 individuals from the Greenlandic population that were historically isolated, combination analyses of Cardio-Metabochip based genotyping and exome sequencing revealed that a common variant in
Recently, whole-genome sequencing in a UK10K-cohort project consisting of 3781 healthy individuals with exome sequencing of 6000 individuals with either rare disease, severe obesity, or neurodevelopmental disorders has been performed to identify low-frequency and rare variants . This project identified 24 million novel genetic variants including novel alleles associated with levels of TAG (
3.5 Epigenetic determinants
Fatty acid-binding proteins (FABPs) play important roles in lipid metabolism and signaling. Dyslipidemia often occurs along with IR, obesity, and hypertension in individuals with MetS. The methylation status of CpG islands of a key regulator of lipid homeostasis,
Meanwhile, malnutrition in childhood, infancy, or fetus affects the prevalence of MetS in adults and their offspring , suggesting that maternal malnutrition affects gene expression in offspring through epigenetic mechanisms.
To date, most studies examining epigenetic changes related to MetS or IR have been conducted in animals and few studies have been conducted in humans. Therefore, further studies in humans are needed in the future.
4. CRISPR screen for genes affecting MetS or IR
Although many GWA studies are widely used to identify genetic loci associated with IR, it remains challenging to identify the causal gene in each locus . Recently, structural and functional connections between GWAS loci and vicinal or distal genes were identified by chromosome conformation capture (3C) technology and expression quantitative trait loci (eQTL) studies [70, 71]. However, the 3C experiments are expensive and the eQTL studies cannot identify all the causal genes for a locus. Moreover, the 2 methods cannot pinpoint the causal genes and mechanisms related to the risk loci of IR. More recently, clustered regularly interspaced short palindromic repeats (CRISPR) knockout screening platform as an alternative method has been applied to pinpoint functions of new candidate causal genes at IR-associated loci in human preadipocytes and adipocytes . This screening platform successfully characterized the functions of 10 new candidate causal genes at IR-associated loci. The 10 candidate genes (
MetS and IR are central risk factors for the development of dyslipidemia, T2D, and CVD as well as complex metabolic traits. Many of the genetic variations implicated in the development of the MetS and IR are associated with glucose and lipid metabolism, respectively. Significant progress has been made in the identification of common and rare genetic variations associated with the MetS and IR in different populations, driven by the advent of GWAS and more recently, genome and exome sequencing approaches.
Despite many scientific efforts in identifying many genetic loci associated with the MetS and IR, their exact molecular pathogenesis remains unclear. Further studies are needed to identify functional links between the genetic variants and the phenotypes and subsequently to uncover the underlying molecular mechanisms of both metabolic disorders.
Clinical validation of the variants identified by several genetic analysis approaches is challenging for reasons resulting from implications by an individual’s lifestyle and environmental factors as well as by genetic factors. In this aspect, studies including larger and more homogeneous populations are needed to identify genetic variants that underlie the association of the various traits of MetS and/or IR. However, results obtained from these studies should be replicated in different populations with a sufficient sample size to avoid false-positive associations and to reduce systematic biases and technical errors.
Approaches such as CRISPR, 3C, and eQTL are being used to identify structural and functional associations between genetic loci discovered by GWAS or exome sequencing and regional or distal genes. Among them, CRISPR as an
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