Current Diabetes Reports

, Volume 12, Issue 1, pp 88–92

Determining Genetic Risk Factors for Pediatric Type 2 Diabetes

Authors

    • Department of Nutrition, Faculty of Medical and Health SciencesUniversity of Auckland
Pediatric Type 2 Diabetes (M Freemark, Section Editor)

DOI: 10.1007/s11892-011-0245-4

Cite this article as:
Morgan, A.R. Curr Diab Rep (2012) 12: 88. doi:10.1007/s11892-011-0245-4

Abstract

The prevalence of type 2 diabetes (T2D) is increasing significantly in the pediatric population. A strong family history of the disease suggests the involvement of genetic factors for diabetes development, but defining the molecular genetics of T2D in children is difficult due to a low number of subjects and the lack of robust diagnostic criteria. Thus, genetic studies of T2D have been carried out almost exclusively in adults. In this review, the genetics of T2D is summarized and options for discovering the missing heritability explored. The review concludes with a discussion of future research that will be required for determining genetic risk factors for pediatric T2D.

Keywords

Pediatric type 2 diabetesGeneticsSusceptibility genesMissing heritabilityGenetic risk factors

Introduction

Type 2 diabetes (T2D) occurs most commonly in adults 40 years or older and in the past was rarely seen in children and young adults. However, in recent years the incidence of pediatric T2D has increased dramatically, fueled by the childhood obesity epidemic [1, 2, 3•]. Obesity is a significant risk factor for T2D. However, there are many obese children who do not develop the disease and some nonobese children who do, indicating that obesity is not the only factor involved in the etiology of pediatric T2D. In addition to obesity, other risk factors for developing pediatric T2D include low birth weight, maternal diabetes, ethnicity, puberty, socioeconomic status, family history, and genetic predisposition [4].

Often, children with T2D have family members who also have the disease. The frequency of a history of T2D in a first- or second-degree relative has ranged from 74% to 100% [5]. This strong family history of the disease suggests the involvement of genetic factors for diabetes development.

Genetics of Type 2 Diabetes

Because pediatric T2D is a relatively recent problem there is a relative paucity of research and the condition is poorly understood. Genetic studies of T2D have been carried out almost exclusively in adults. Defining the molecular genetics of T2D in children is difficult due to a low number of subjects and the lack of robust diagnostic criteria. However, it is likely that T2D is caused by the same genetic factors in both children and adults. A recent study showed that the majority of novel fasting glucose loci identified in genome-wide association studies (GWAS) of normoglycemic adults are detectable in childhood, with effect sizes comparable to those reported in replication studies of adults [6••]. Whether the same is true for T2D loci, which do not necessarily overlap with loci that regulate fasting glucose in nondiabetic individuals, remains largely an open question.

One study that has undertaken genetic association testing with pediatric T2D examined transcription factor 7-like 2 (TCF7L2) variants in non-Hispanic white (NHW) and African American (AA) youths [7•]. The mean age was 15.7 years for those with diabetes and 14.5 years for controls. They found that TCF7L2 variation was associated with an increased risk of T2D among AA youth but did not find a significant association in NHW youth. These data are consistent with a previous study in Finland among 11 young adults with T2D [8]. Although there were a small number of cases the results indicated that TCF7L2 variation may play an important role in cases of early-onset T2D. Further genetic association studies are required to investigate other T2D susceptibility genes in children.

In recent years, the search for genetic determinants of T2D has changed dramatically. Although linkage and small-scale candidate gene studies were highly successful in the identification of genes associated with monogenic forms of T2D, they were far less successful when applied to the more common forms of the disease. Progress was made with the introduction of GWAS technology, which has yielded excellent results. Approximately 40 loci have now emerged from various GWAS of T2D [921]. Table 1 provides a list of genes linked to susceptibility to T2D.
Table 1

Type 2 diabetes susceptibility genes

Study

Gene symbol

Gene name

References

Mode of action

Linkage

TCF7L2

Transcription factor 7-like 2 (T-cell-specific, HMG-box)

[11, 20, 32]

β-cell function

Candidate gene

IRS1

Insulin receptor substrate 1

[18, 20, 33]

Insulin sensitivity

Candidate gene

PPARG

Peroxisome proliferator-activated receptor-γ

[11, 20, 34]

Insulin sensitivity

Candidate gene

KCNJ11

Potassium inwardly-rectifying channel, subfamily J, member 11

[11, 20, 35]

β-cell function

Candidate gene

WFS1

Wolfram syndrome 1 (wolframin)

[20, 36, 37]

β-cell function

Candidate gene

HNF1B

HNF1 homeobox B

[14, 20, 38]

Unknown

GWAS

FTO

Fat mass and obesity associated

[1012]

Adiposity

GWAS

CDKN2A/2B

Cyclin-dependent kinase inhibitor 2A/2B

[11, 12, 20]

β-cell function

GWAS

IGF2BP2

Insulin-like growth factor 2 mRNA binding protein 2

[11, 12, 20]

β-cell function

GWAS

CDKAL1

CDK5 regulatory subunit associated protein 1-like 1

[1113, 20]

β-cell function

GWAS

HHEX

Hematopoietically expressed homeobox

[9, 11, 20]

β-cell function

GWAS

SLC30A8

Solute carrier family 30 (zinc transporter), member 8

[9, 11, 19, 20]

β-cell function

GWAS

KCNQ1

Potassium voltage-gated channel, KQT-like subfamily, member 1

[16, 17, 20]

β-cell function

GWAS

MTNR1B

Melatonin receptor 1B

[3941]

β-cell function

GWAS meta-analysis

ADAMTS9

ADAM metallopeptidase with thrombospondin type 1 motif, 9

[15, 20]

Insulin sensitivity

GWAS meta-analysis

CDC123/CAMK1D

Cell division cycle 123 homolog/calcium/calmodulin-dependent protein kinase ID

[15, 20]

β-cell function

GWAS meta-analysis

JAZF1

JAZF zinc finger 1

[15, 20]

β-cell function

GWAS meta-analysis

NOTCH2/ADAM30

Notch 2/ADAM metallopeptidase domain 30

[15, 20]

Unknown

GWAS meta-analysis

THADA

Thyroid adenoma associated

[15, 20]

β-cell function

GWAS meta-analysis

TSPAN8/LGR5

Tetraspanin 8/leucine-rich repeat-containing G protein-coupled receptor 5

[15, 20]

β-cell function

GWAS meta-analysis

ADCY5

Adenylate cyclase 5

[19]

Unknown

GWAS meta-analysis

PROX1

Prospero homeobox 1

[19]

β-cell function

GWAS meta-analysis

GCK

Glucokinase

[19]

β-cell function

GWAS meta-analysis

GCKR

Glucokinase regulatory protein

[19]

Insulin sensitivity

GWAS meta-analysis

DGKB/TMEM195

Diacylglycerol kinase, β/transmembrane protein 195

[19]

β-cell function

GWAS meta-analysis

ARAP1 (CENTD2)

ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1

[20]

β-cell function

GWAS meta-analysis

BCL11A

B-cell CLL/lymphoma 11A (zinc finger protein)

[20]

Unknown

GWAS meta-analysis

CHCHD9/TLE4

Coiled-coil-helix-coil-coiled-helix domain containing 9/transducin-like enhancer of split 4

[20]

Unknown

GWAS meta-analysis

DUSP9

Dual specificity phosphatase 9

[20]

Unknown

GWAS meta-analysis

HMGA2

High mobility group AT-hook 2

[20]

Unknown

GWAS meta-analysis

HNF1A

HNF1 homeobox A

[20]

Unknown

GWAS meta-analysis

KLF14

Krüppel-like factor 14

[20]

Unknown

GWAS meta-analysis

PRC1

Protein regulator of cytokinesis 1

[20]

Unknown

GWAS meta-analysis

TP53INP1

Tumor protein p53 inducible nuclear protein 1

[20]

Unknown

GWAS meta-analysis

ZBED3

Zinc finger, BED-type containing 3

[20]

Unknown

GWAS meta-analysis

ZFAND6

Zinc finger, AN1-type domain 6

[20]

Unknown

GWAS meta-analysis

RBMS1

RNA binding motif, single-stranded interacting protein 1

[21]

Unknown

GWAS genome-wide association study

TCF7L2 is the most important T2D susceptibility gene identified to date, with genetic variants strongly associated with diabetes in all major racial groups, with the possible exception of Native Americans. It is the most consistent signal across various GWAS and is associated with the highest risk of developing T2D. It also can predict the likelihood that a person will convert from a state of prediabetes (borderline blood sugar levels) to full-blown T2D. Several studies have shown that overweight individuals with prediabetes who have certain TCF7L2 variants have a 55% to 70% chance of developing T2D within 3 to 5 years after their initial diagnosis [22, 23]. How TCF7L2 affects the development of T2D is not completely understood, but it is thought to act through impairment of insulin secretion due to β-cell dysfunction. This appears to be a common mode of action for many of the T2D susceptibility genes, including CDC123/CAMK1D, CDKAL1, CDKN2A/B, CENTD2, DGKB/TMEM195, GCK, HHEX, IGF2BP2, JAZF1, KCNJ11, KCNQ1, MTNR1B, SLC30A8, TCF7L2, THADA, TSPAN8/LGR5, PROX1, and WFS1. It is not clear how all of these genes influence β-cell function, but they may regulate β-cell proliferation, regeneration, and/or apoptosis. Defects in β-cell growth, survival, and/or function would result in a reduced insulin-secretory capacity. Other T2D susceptibility genes modulate adiposity and/or insulin sensitivity: these include FTO, ADAMTS9, GCKR, IRS1, and PPARG. But function remains to be determined for many of the most recently identified genes. Important next steps will employ clinical, cellular, animal, and molecular models to investigate the mechanisms underlying the associations between these susceptibility genes and T2D.

There should be little disagreement that GWAS have helped to advance the field of T2D genetics: from only a handful of genuine associations in 2005, there are now approximately 40 regions of the genome displaying replicated associations. However, it is evident that much of the genetic risk remains unexplained; this is now commonly referred to as the “missing heritability” [24, 25•]. Uncovering this missing heritability is essential to the progress of T2D genetic studies and subsequently to the translation of genetic information into clinical practice. It had been suggested that structural variation, including copy number variants such as insertions and deletions, may account for some of the unexplained heritability. However, there is little evidence that such copy number variants contribute in a major way to T2D risk beyond what has already been discovered by single nucleotide polymorphism (SNP) arrays [26].

Rare variants are obvious contenders for the source of the missing heritability. Because the GWAS approach depends on the ability of existing arrays to capture ungenotyped variants, which are more easily tagged if they are common in the population, rare variants would likely be missed. It is now clear that new studies are needed to decipher these rare variants, either using arrays containing rare variants or high-throughput whole-genome sequencing methods.

Common variants with small individual effects might contribute more substantially to disease risk through interactions with other genetic variants. Thus, the effects of common or rare variants might be missed by examining single loci independently. Clever analytical techniques for analyzing gene-gene interactions or pathways are needed and such methods have begun to be tested [2729], with more complex analytical methods being developed all the time.

There is a growing body of literature suggesting a role for epigenetic factors in the complex interplay between genes and the environment. Epigenetic effects are defined as chemical modifications of DNA and its associated proteins that can alter the expression of genes, and thus physical traits, without changing gene sequence. Unlike sequence changes, they can be reset or undone under certain conditions such as in early development. Mechanisms include changes in histone deacetylation and methylation of cytosines in CpG clusters [30]. A recent study has provided the first evidence that methylation status of gene promoters in utero can affect related phenotypes later in a child’s development [31••].

There remains a lot more work to do in researching the genetics of T2D. Future progress in gene mapping will probably involve a combination of larger GWAS and deep sequencing, alongside complex statistical and bioinformatic analytical approaches. But it is likely that it will be targeted genotyping approaches, such as that being undertaken by the large international Metabochip effort, which will enable sufficient genetic resolution for substantial insights. The Metabochip is a custom Illumina array (Illumina, Inc., San Diego, CA) containing approximately 217,000 SNPs, designed to support large-scale follow-up of putative associations for T2D as well as other metabolic and cardiovascular traits. The project is spread across various international consortia and aims to allow replication of T2D (plus other metabolic diseases) GWAS data and to provide dense SNP maps around known genome-wide significant disease-associated loci to permit fine mapping. The project will also facilitate the identification of pleiotropic genes associated with more than one metabolic disease.

Conclusions

There is clear evidence from epidemiologic studies that the susceptibility to T2D in children and adolescents is determined by genetic factors as well as changes in lifestyle. Recently, there has been considerable progress in defining novel genes for adult-onset T2D, but these studies have required thousands of cases and controls. It is likely that the same genes in the adult population are involved in pediatric T2D; however, large multicenter collaborative genetic studies in children and adolescents are needed to determine if T2D risk in children is attributable to the same or different gene variants as T2D risk in later life, and whether the genetic contribution to disease susceptibility is similar to that in adulthood.

Disclosure

No potential conflict of interest relevant to this article was reported.

Copyright information

© Springer Science+Business Media, LLC 2011