University of Southern California Dissertations and Theses

The development of type 2 diabetes mellitus (type 2 DM) in California twins

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The development of type 2 diabetes mellitus (type 2 DM) in California twins

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THE DEVELOPMENT OF TYPE 2 DIABETES MELLITUS (TYPE 2 DM) IN
CALIFORNIA TWINS

by

Pei-Jung Tsai

May 2011
Copyright 2011 Pei-Jung Tsai
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements of the Degree
DOCTOR OF PHILOSOPHY
(EPIDEMIOLOGY)
ii

Acknowledgements

The California Twin Program (CTP) was funded by grants from the California Tobacco-
based Disease Research Program (8RT-0107H and 6RT-0354H). The CTP has been
recognized by an Outstanding Investigator Award (R35CA42582) and an Environmental
Health Sciences Center Award (5P30ES07048 NIEHS). We thank the initial project
management team and the many twins who participated.

iii

Table of Contents

Acknowledgements ii
List of Tables v
List of Figures vi
Abstract vii
Chapter 1: Introduction 1
Chapter 2: Genetic Susceptibility in Type 2 Diabetes: A Literature Review 3
2.1 Introduction 3
2.2 Type 2 Diabetes: Arising from Environmental and Genetic Exposures 5
2.3 Prediction of Type 2 Diabetes Development 7
2.4 Inheritability of Type 2 Diabetes 8
2.5 Thrifty Genotypes and Phenotypes 9
2.6 Mapping Genetic Variability 10
2.7 Calapin 10 and Type 2 Diabetes 11
2.8 Common Variants in MODY Genes 12
2.9 The Genome-Wide Association Studies (GWAS) 13
2.10 Additional Loci Associated with Quantitative Metabolic Traits 27
2.11 Whole Genome Association Studies 27
2.12 Obesity Genes and Type 2 Diabetes 31
2.13 Genetic Influences in Mitochondrial Dysfunction 34
2.14 Gene-Environment Interaction 36
2.15 The Emerging Genetic Architecture of Type 2 Diabetes 36
2.16 Insights into Pathogenesis from GWAS Studies 40
2.17 Pharmacogenetics 42
iv

2.18 Genetic Prediction of Type 2 Diabetes 42
2.19 Future Directions 43
Chapter 3: Paper One 45
3.1 Abstract 45
3.2 Introduction 46
3.3 Materials, Methods, and Results 48
3.4 Discussion 59
Chapter 4: Paper Two 65
4.1 Abstract 65
4.2 Introduction 66
4.3 Materials, Methods, and Results 68
4.4 Discussion 77
Chapter 5: Conclusion 82
Bibliography 84
v

List of Tables

Table 1: Identifying the Genes for Type 2 Diabetes 12

Table 2: Genetic Loci Implicated in “Common Variety” Type 2 Diabetes 14

Table 2: Continued - Genetic Loci Implicated in “Common Variety”
Type 2 Diabetes 15

Table 3: Age Distribution and Proband Concordance for Type 2 Diabetes
at Baseline (1997) and After the Follow-UP Study (2001) by
Zygosity 55

Table 4: Percent with Family Members Affected by Type 2 Diabetes by
Family Relation ship and Type 2 Diabetes 59

Table 5: Number of Type 2 Diabetes Discordant Pairs by Gender and Mean
Age at Baseline (1992) by Zygosity 73

Table 6: Adjusted Odds Ratios for Selected Adult Lifestyle Factors in
Relation to the Development of Type 2 Diabetes among 95
DZ Discordant Like-sex Twins 75

Table 7: Adjusted Odds Ratio of Adolescent Smoking, and Puberty Growth
in Relation to the Development of Type 2 Diabetes by Zygosity 77
vi

List of Figures

Figure 1: A Two-step Model for the Development of Type 2 Diabetes 7

Figure 2: Probability of Twin Remaining Diabetes Discordant by
Time Since First Diagnosis in Pair Zygosity (Monozygotic
twins=MZ vs. Dizygotic twins=DZ; p-value<0.0001) 57

Figure 3: Probability of MZ Pairs Remaining Diabetes Discordant by
Years Sharing A Room Growing Up (<16 years=
seproom1 vs. 16+ years=seproom2; p-value<0.0177) 58

vii

Abstract

Previous studies have shown that the development of type 2 diabetes mellitus (Type 2
DM) is affected by multiple genetic and environmental factors. Numerous family and
twin studies have provided evidence showing that the risk of Type 2 DM is elevated
among families and twins if another family member or co-twin has the disease
[Serjeantson et al. 1983]. The familial aggregation [Cauchi et al. 2008, Watanabe et al.
2007, Yi et al. 2005, Prunier et al. 2004, Prestwich et al. 2007, Cossu et al. 1999, McLin
et al. 2007] and higher concordance rate found in monozygotic (MZ) twins compares to
dizygotic (DZ) twins [Fehmann et al. 1995, Florez et al. 2006, Lyssenko et al. 2007, Shu
et al. 2008, Cauchi et al. 2008, Chimieti et al. 2005, Sladek et al. 2007, Steinthorsdottir et
al. 2007, Chimienti et al. 2006, Taylor et al. 2005, Simon et al. 2001, Raz et al. 1989]
points to the possibility of genetic influence in the development of Type 2 DM. However,
familial aggregation and twin concordance support not only a genetic factor in the
development of Type 2 DM but also the possibility of shared environmental influences.
Despite the attempts by various types of studies including family studies [Cauchi et al.
2008, Watanabe et al. 2007, Yi et al. 2005, Prunier et al. 2004, Prestwich et al. 2007,
Cossu et al. 1999, McLin et al. 2007], twin studies [Fehmann et al. 1995, Florez et al.
2006, Lyssenko et al. 2007, Shu et al. 2008, Cauchi et al. 2008, Chimieti et al. 2005,
Sladek et al. 2007, Steinthorsdottir et al. 2007, Chimienti et al. 2006, Taylor et al. 2005,
Simon et al. 2001, Raz et al. 1989), observational studies (Martin et al. 1992, Assd et al.
1991, Ling et al. 2004, Carlsson et al. 2005, Winckler et al. 2007, Deeb et al. 1998,
viii

Altshuler et al. 2000, Yasuda et al. 2008, Unoki et al. 2008, Sandhu et al. 2007], genetic
linkage studies [Pearson et al. 2007, Lyssenko et al. 2005, Ahlzen et al. 2008, Weedon et
al. 2006, Wasserfall et al. 2006, Ahmann et al. 2006, Blonde et al. 2007, Gottlieb et al.
1968, Tattersall et al. 1972, Barnett et al. 1981, Newman et al. 1987], and association
studies [Poulsen et al. 1999, poulsen et al. 2009, Condon et al. 2008, Serjeantson et al.
1984, Stancakova et al. 2009, Gloyn et al. 2008, Schmidt et al. 2008], the mechanisms
involving genetic factors and environmental influences on the development of Type 2
DM are still unclear. Chapter 1 provides the introduction to the background of the
literature review and the 2 papers conducted to examine the dissertation hypotheses.
Chapter 2 provides a literature review of the genetic susceptibility in type 2 diabetes
including identified and known candidate genes and genetic variants found in different
geographic populations in the past decades or so.
Chapter 3 provides the details of the study designs, statistical analysis, study results, and
discussion in a draft paper tilted “Genetic Susceptibility to Type 2 Diabetes May Be
Modified by Shared Early Environment”. Briefly, the background, methods, results, and
conclusion is as following:
Background: Genetic and environmental factors have been implicated in development of
type 2 diabetes. We assessed these factors using a population-based twin registry.
Methods: A follow-up study of 92 monozygotic (MZ) and 198 dizygotic (DZ) pairs with
self-reported type 2 diabetes in at least one member was conducted. The twins were first
ascertained in 1992 from the California Twin Program (CTP), and the follow-up dates
were from January, 1997 to December, 2001. The concordance rates and disease
ix

discordant probability of initially unaffected co-twins were examined using the proband
method and Kaplan-Meier method, respectively.
Results: Genetic susceptibility to type 2 diabetes was suggested by higher concordance
rates in MZ compared to DZ twins at baseline and after follow-up (53% vs. 36%, and
77% vs. 53%); lower disease discordant probability after 15 years in MZ (22%) vs. DZ
twins (58%) (p<0.0001); a shorter time interval between the first and second diagnosis in
MZ (5.79+4.32 years) compared to DZ concordant pairs (7.39+4.17 years) (p= 0.037);
and a higher proportion of parents with diabetes among concordant pairs than discordant
pairs. However, among MZ twins, disease discordant probability was longer among those
who shared the same room for 16 years or longer compared to those who moved apart
sooner (28.8% vs. 7.6%) (p=0.0177).
Conclusions: Our findings support prior estimates of type 2 diabetes concordance in MZ
twins but suggest that concordance in DZ twins may have been underestimated.
Furthermore, disease discordant probability may be modified by shared early
environmental factors which remain to be determined.
Chapter 4 provides the details of the study designs, statistical analysis, study results, and
discussion in a draft paper tilted “The Smoking and Height at Teens and Type 2 Diabetes
Risk in California Twins”. Briefly, the background, methods, results, and conclusion is
as following:
Background: Type 2 diabetes is thought to be a heterogeneous and complex disease
presumably with a multi-factorial etiology consisting of genetic and environmental
factors and often manifest later in adult life. This current study is aimed to assess the
x

association of environmental factors and type 2 diabetes adjusting for genetic
susceptibility by using population-based twin registry.
Methods: This was a matched case-control study nested in the California Twin Program
(CTP) and conditional multivariate logistic regressions were used to determine odds
ratios for childhood/adolescent and adult lifestyle factors in 161 discordant twin pairs
(126 dizygotic (DZ) and 35 monozygotic (MZ) pairs).
Results: The faster growth rate and taller in height at teens was associated with an
increased risk of developing type 2 diabetes in MZ (Matched Odds Ratio (OR)=20.04,
95% confidence interval (CI): 2.01-72.28) and DZ (Matched OR=8.74, 95%CI: 4.48-
17.04) discordant twin pairs. In addition, adolescent smoking was also associated with an
increased risk for type 2 diabetes in DZ discordant twins (Matched OR=1.72, 95%CI:
1.04-2.85).
Conclusions: Irrespective of the age of type 2 diabetes onset in adulthood (i.e. after age
35), childhood/adolescent as well as adult lifestyle factors were associated with Type 2
diabetes; therefore indicating the potential importance of early prevention efforts.
Chapter 5 provides the details of the summary, conclusions, and future plans.

1

Chapter 1: Introduction

This proposed dissertation plan is a cohort study of native California twins with Type 2
DM to evaluated possible changes in concordance of MZ and DZ twins over a period of
follow-up to determine whether the concordance in MZ and DZ twins changes with
increasing age. Furthermore, using 1-1 matched case-control study design nested in the
same cohort study, the association between these risk factors and the development of
Type 2 DM in twins (MZ and DZ) has been evaluated with available information for
various known Type 2 DM risk factors such as obesity, diet, physical activity, and other
lifestyle factors including alcohol consumption, and cigarette smoking. This design is
unique in that genetic factors can be controlled in total among MZ twins who share 100%
of their genes and in part among DZ twins who share 50% of their genes. The details of
the study designs, statistical analysis, results, and discussions are described in chapters 3
and 4. Briefly, the twin subjects in this proposed study were the self-reported type 2
diabetes patients based on the baseline questionnaires from the California Twin Program
(CTP) in 1992 who were later followed up annually from January, 1997 to December,
2002 to obtain specific information on their self-reported and their co-twins type 2
diabetes including the age at diagnosis, the used diagnostic methods, the prescribed
diabetes treatments, birth weight, and family history of diabetes. Prior to the statistical
analysis, a set of type 2 diabetes criteria was developed to validate the self-reported type
diabetes based on the follow-up information from 1997-2002 and the proxy information
2

of co-twin type 2 diabetes was also validated. In twin subjects with valid baseline/follow-
up information and type 2 diabetes diagnosis, the probandwise method was used to
estimate the changed concordances over follow-up period for MZ and DZ twins, the z-
test statistics was used to examine/describe the family history of type 2 diabetes for
discordant/concordant pairs in MZ and DZ twins, and the conditional logistic regression
was used to examine the development of type 2 diabetes and its association to the
environmental/behavioral factors in adult and adolescent life among discordant pairs in
MZ and DZ twins.

3

Chapter 2: Genetic Susceptibility in Type 2 Diabetes: A
Literature Review
2.1 Introduction
Diabetes mellitus is reported to be among the five leading causes of death in most
countries including United States [American Diabetes association. 2001, Clark et al.
1998]. Diabetes is generally believed to be a heterogeneous group of metabolic disorders
characterized by hyperglycemia resulting from defects in insulin secretion, insulin action,
or both. If left untreated, the persistent hyperglycemia leads to a variety of complications
such as nephropathy, retinopathy, neuropathy, and angiopathy. Diabetes is also a
significant risk factor for other common disorders, such as coronary artery and
cardiovascular diseases and hypertension. Pathogenic progress involved in the
development of diabetes ranges from autoimmune destruction of the pancreatic beta-cells
with consequent insulin deficiency, to abnormalities in insulin signaling resulting in
resistance to insulin action. The vast majority of diabetic cases fall into two broad
etiopathogenic groups. Type 1 diabetes, formerly known as insulin-dependent diabetes
mellitus (IDDM), accounting for <10% of diabetes is caused by an absolute deficiency of
insulin secretion [The Expert Committee on the Diagnosis and Classification of Diabetes
Mellitus. 1999, WHO study group 1985] and is most prevalent in children and
adolescents, and European, especially of Scandinavian descent. Type 2 diabetes,
formerly known as non-insulin-independent diabetes mellitus (NIDDM), is the most
common form of diabetes accounting for 90-95% of all cases [The Expert Committee on
4

the Diagnosis and Classification of Diabetes Mellitus. 1999, WHO study group 1985] and
results from a combination of resistance to insulin action with inadequate compensatory
insulin secretory response. Type 2 diabetes is usually seen in middle aged population and
thought to be an adult-onset disease.
The overall prevalence of type 2 diabetes in the US is about 6 % including 2% with
undiagnosed diabetes [American Diabetes association. 2001, Clark et al. 1998, The
Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. 1999].
Diabetes prevalence and incidence increases with increasing age and incidence increases
with age until about 70 years old then starts to decline. The age at peak incidence
generally varies with type of diabetes (childhood for type 1 diabetes and age of 65-74
years for type 2 diabetes) and ethnic group (earlier for high-risk group such as Pima
Indians peaking at 40-50 years old) [Knowler et al. 1990, King et al. 1993, Harris et al.
1998, Flegal et al. 1991]. The type 2 diabetes prevalence has continually increased over
the past decade, and varies considerably with unusual high prevalence (about 50%) in
Pima Indians residing in Arizona [Knowler et al. 1990] and Micronesian Nauruans living
in Central Pacific (about 35%) [King et al. 1993]. In the United States, prevalence in
Hispanic-Americans (14%) is higher than non-Hispanic whites (5%) or African-
Americans (8.5%) [Harris et al. 1998, Flegal et al. 1991]. Dramatic increases in
prevalence for immigrants over a short period of time [King et al. 1993, Harris et al.
1998] often reflect the rapidly changing lifestyles (high-calorie diet and physical
inactivity) in some of these populations.
5

Familial clustering and twin studies have suggested a genetic component for type 2
diabetes [Rewers et al. 1995]. However, identifying genetic susceptibility loci has been
difficult due to genetic heterogeneity and the context-dependency of disease expression,
which is strongly related to environmental factors, such as lifestyle. Considerable effort
has been directed toward genetic approaches to uncover the mechanisms causing type 2
diabetes. Despite the complexity, there have been recent advances in defining genetic
determinants, including characterization of rare monogenic forms, definition of important
candidate genes, specification of chromosomal loci harboring diabetes susceptibility
genes, and insights from the study of isolated populations.

2.2 Type 2 Diabetes: Arising from Environmental and Genetic
Exposures
Unlike type 1 diabetes, which is caused by insulin deficiency due to autoimmune
destruction of pancreatic beta cells, type 2 diabetes arises from an impairment in the
ability of muscle, fat, and liver to respond to insulin, e.g., insulin resistance, combined
with an inability of the beta cell to respond normally to glucose by appropriately
increasing insulin secretion [Kahn et al. 1994]. While the relative contribution of these
two defects to diabetes pathogenesis continues to be debated, longitudinal studies in high-
risk individuals suggest that insulin resistance is an early phenomenon, occurring years
before any evidence of glucose intolerance, whereas the bets cell failure develops later in
the pathogenesis of disease [Martin et al. 1992]. On the other hand, other studies have
6

shown that the disposition index, reflecting both insulin sensitivity and insulin secretion,
is an early marker of and predictor of type 2 diabetes [Lyssenko et al. 2005, Bergman et
al. 2007].
Both insulin resistance and beta cell failure are thought to result from complex interplay
of many different pathways [Saad et al. 1991] under the combined control of
environmental and genetic factors (Figure 1). The role of genetics in type 2 diabetes is
indicated by familial clustering of insulin sensitivity and insulin secretion, the higher
concordance rate of type 2 diabetes in monozygotic versus dizygotic twins, and the high
prevalence of type 2 diabetes in certain ethnic groups (e.g., Pima Indians or Mexican
Americans) [Weijnen et al. 2002, Flegal et al. 1991b]. It has been estimated that 30-70%
of type 2 diabetes risk can be attributed to genetics [Poulsen et al. 1999]. Patterns of
inheritance suggest that type 2 diabetes is both polygenetic and heterogeneous which
multiple genes are involved and different combinations of genes play a role in different
subsets of individuals. Exactly how many genes and what their relative contributions are,
however, remains uncertain, with some investigators postulating that type 2 diabetes
consists of two or three major types with some minor types [Kahn et al. 1994, Rich et al.
1990].

7

Figure 1: A Two-step Model for the Development of Type 2 Diabetes

Genetic susceptibility Environmental factors

Step 1

Limited beta-cell mass
Obesity Glucotoxicity?
Physical inactivity Inherited beta-cell defect
Diet? Aging
Age

Step 2

2.3 Prediction of Type 2 Diabetes Development
The importance of different risk factors for type 2 diabetes differ between ethnic
populations by the increased risk conferred by a family history of diabetes seems more
constant [Lyssenko et al. 2005], although its relative effect decreases with increasing
frequency of type 2 diabetes in the population. The predictive value of a family history
of type 2 diabetes is also relatively poor in young subjects whose parents have not yet
developed the disease. A low level of physical activity, abdominal obesity and presence
Insulin resistance
Impaired Glucose
Tolerance
Type 2 Diabetes
Beta-cell
Defect
PPARγ gene
Differentiation of adipose tissue
8

of the metabolic syndrome also commonly confer an increased risk of type 2 diabetes
[Laaksonen et al. 2002, Lorenzo et al. 2003]. In addition, an elevated glucose
concentration per se is a strong predictor of future type 2 diabetes [Lyssenko et al. 2005,
Groop et al. 1996, Israeli Research Group 2005]. In a prospective study of 2,115 non-
diabetic individuals followed for six years within the Botnia study it could show that
individuals with a family history of type 2 diabetes, with a BMI>30kg/m
2
, and fasting
plasma glucose concentration>5.5 mmol/l had a 16-fold increased risk of developing type
2 diabetes [Lyssenko et al. 2005].

2.4 Inheritability of Type 2 Diabetes
There is ample evidence that type 2 diabetes has a strong genetic component. The
concordance of type 2 diabetes in monozygotic twins is approximately 70% compared
with 20-30% in dizygotic twins [Newman et al, 1987, Kaprio et al. 1992]. Given the age-
dependent penetrance of the disease, it is clear that the longer the follow-up, the higher
the concordance rate of type 2 diabetes clusters in families [Kaprio et al. 1992]. The life-
time risk of developing the disease is about 40% in offspring of one parents with type 2
diabetes [Kobberling et al. 1982], greater if the mother is affected [Groop et al. 1996], the
risk approaching 70% if both parents have diabetes. Translated into a lamda-s-value, the
recurrence risk of a sibling of an affected person divided by the risk for the general
population, this means that a first-degree relative of a patient with type 2 diabetes has a 3-
fold increased risk of developing the disease [Lyssenko et al. 2005]. Large ethnic
differences in the prevalence of type 2 diabetes have also been ascribed to s genetic
9

component [Serjeantson et al. 1983, Borsseau et al. 1979]. The change in the
environment towards a more affluent western life style plays a key role in the epidemic
increase in the prevalence of type 2 diabetes worldwide. This change has occurred
during this period but this does not exclude an important role for genes in the rapid
increased in type 2 diabetes, since genes or gene variants explain how we respond to the
environment.

2.5 Thrifty Genotypes and Phenotypes
A plausible explanation for the interaction between genes and environment comes from
the thrifty gene hypothesis. Neel [Neel et al. 1962] proposed that individuals living in an
environment with unstable food supply (as for hunters and nomads) would maximize
their probability of survival if they could maximize storage of energy. Genetic selection
would thus favor energy-conserving genotypes in such environments. Storage of energy
as fat, especially as intra-abdominal fat, is a more efficient way of storing energy than as
glycogen in muscle and liver. This is what we find in the insulin-resistant phenotype and
explains why offspring of subjects with type 2 diabetes show early accumulation of
abdominal fat [Groop et al. 1996].
An alternative explanation has been proposed by which these changes can be
consequence of intrauterine programming, the so called thrifty phenotype hypothesis
[Hales et al. 1992]. According to this hypothesis, intrauterine malnutrition would lead to
a low birth weight and increased risk of the metabolic syndrome (clustering of
cardiovascular risk factors like abdominal obesity, dyslipidemia, hypertension and
10

glucose intolerance) later in life. These findings have been replicated in several studies
but it has also been shown that the risk of a small birth weight is increased in families
with the metabolic syndrome [Melander et al. 1999] suggesting that a small birth weight
could be a phenotype for a thrifty gene.

2.6 Mapping Genetic Variability
Traditionally, two different methods have been used to discover genes tat are related to a
disease: analysis of genomic regions shared by relative more often than expected (so
called linkage analysis using polymorphic markers such as micro satellites or tandem
repeats) and candidate gene studies, particularly by attempts to correlate biological
variation (phenotype) with variation in DNA sequences (genotype) in the form of a single
nucleotide polymorphism (SNP). The most straightforward approach would be to
sequence that whole genome in affected and unaffected individuals which is in
developing. The situation may change rapidly with introduction of new large-scale
sequencing tools like the Solexa sequencing. Several approaches have been described to
estimate whether an observed association can account for linkage [Li et al. 2004].
Without functional support it is not always possible to know whether linkage and
association represent the genetic cause of the disease. For many complex disorders this
may require a cumbersome sequence of in vitro and in vivo studies. Over the past 2
decades four approaches have been to unravel the genetics of type 2 diabetes, each with
some success (Table 1) and further genetic studies to follow. For instance, the initial
11

success of identifying type 2 diabetes susceptibility genes by linkage has been marked by
the story of one single gene; CAPN10.

2.7 Calapin 10 and Type 2 Diabetes
Considered to be a successful genome wide scan of type 2 diabetes, Graeme Bell and co-
workers reported in 1996 significant linkage of type 2 diabetes in Mexican American sib
pairs to a locus on chromosome 2q37, which is called NIDDM1 [Hanis et al. 1996]. A
re-examination of the data suggested an interaction with another locus on chromosome 15
[Cox et al. 1999]. This enabled the researchers to narrow the telomeric region with high
recombination rate down to 7cM, which luckily represented only 1.7 megabases of
physical DNA. To clone the underlying gene they genotyped 21 SNPs and identified a
three-marker haplotype which was nominally associated with type 2 diabetes. At the end,
three intronic SNPs [Bouchard et al. 1988, Carey et al. 1996, Ling et al. 2004] in the gene
coding for calpain 10 (CAPN10) could explain most of the linkage [Horikawa et al.
2000]. Calpain 10, a cystein protease with largely unknown functions in glucose
metabolism, was no obvious candidate gene for type 2 diabetes. Despite a number of
subsequent negative studies, several meta-analyses have shown consistent association of
SNPs 43 and 44 with type 2 diabetes [Groop et al. 1996, Parikh et al. 2004]. Neither was
it easy to understand how intronic variation in this gene could increase risk. Carrying the
G allele of SNP43 is associated with decreased expression of the gene in skeletal muscle
and insulin resistance [Carlsson et al. 2005]. How this translates into increased risk of
type 2 diabetes is not known and will require further functional studies. CAPN10 has not
12

been confirmed as a candidate gene for type 2 diabetes by means of Genome-Wide
Association Study (GWAS).

Table 1: Identifying the Genes for Type 2 Diabetes
Studies of Monogenic Forms of Type 2 Diabetes
Maturity Onset Diabetes of the Young (MODY)
Rare forms of insulin resistance
Neonatal diabetes
Mitochondrial syndromes of diabetes
Candidate Gene Approach
Functional
Genes involved in insulin action and insulin secretion
Genes associated with diseases associated with diabetes (obesity, genetic syndrome, etc)
Genes identified in animal models of diabetes
Positional
Genes in linked intervals identified through family studies
Useful for monogenic forms of diabetes (e.g., MODY)
Challenging for common, multifactorial Type 2 diabetes
Finding Genes with Altered Levels of Expression
Subtraction cloning and differential display
Microarray analysis (gene expression arrays)
Genome-wide Association Studies
Microarry-based typing of > 100K SNPs spanning the entire genome
Large populations of diabetic cases and non-diabetic controls
Populations with intermediate phenotypes – obesity, insulin resistance, polycystic ovarian disease

2.8 Common Variants in MODY Genes
There are at least six forms of MODY which are caused by mutations in s distinct gene.
Apart from MODY2, which is caused by mutations in the glucokinase gene, most other
forms of MODY are caused by mutations in different transcription factors like HNF4-
alpha (MODY1), HNF1-alpha (MODY3), IPF-1 (MODY4), HNF1-beta (MODY5), and
NeuroD (MODY6). Common to all of them is that they result in impaired insulin
secretion and usually show strong allelic heterogeneity, i.e. different mutations cause the
disease in different families. It was therefore logical to study whether more “mild”
13

variations in these genes could contribute to late-onset type 2 diabetes. This turned out to
be the case, at least for common variants in HNF1-alpha and HNF4-alpha [Winckler et al.
2007]. These studies not only emphasized the need for large sample size but also the
need to consider BMI and age in the statistical analysis which genes causing subtle
defects in insulin secretion are more likely to be unmasked in individuals with increased
insulin needs, i.e. in insulin-resistant individuals [Holmkvist et al. 2006].

2.9 The Genome-Wide Association Studies (GWAS)
The starting point for the candidate gene approach is the potential implications that either
altered expression and/or function of a particular gene product (conferred by intronic or
exonic genetic variants) may have on a biological function or disease. Extending the
analysis of genes implicated in monogenic forms of diabetes has proved successful also
for type 2 diabetes as exemplified by PPAR-gamma, KCNJ11, TCF2/HNF1B , WFS1
and others. Over these years of the GWAS studies, there are various genetic loci
suggested in the common mechanism of type 2 diabetes development (Table 2). Among
these, various genetic loci have been studied further as its potential implication and role
in the pathogenesis of type 2 diabetes.

14

Table 2: Genetic Loci Implicated in “Common Variety” Type 2 Diabetes
Chromosome
Location
Gene
Symbol
Common/Other
Name
Cellular Function OMIM
Number
OR Frequency
of Risk
Allele
1p12 NOTCH2 Notch 2
preproprotein
Regulator of cell
differentiation
600276 1.13 0.11
2p21 THADA Thyroid
adenoma-
associated gene
Unknown 611800 1.15 0.90
3p14 ADAMTS
9
Disintegrin-like
and
metalloproteinas
e with
thrombospondin
type 1 motif
Proteolytic enzyme
regulating
extracellular matrix
605421 1.09 0.76
3p25 PPARG Peroxisome
proliferator
activating
receptor gamma
(PPARγ)
Transcription factor
receptor for TZDs
and prostaglandins
601487 1.17 0.85
3q28 IGF2BP2 IMP2 IGF2 mRNA-
binding protein 2
608289 1.14 0.29
6p22.3 CDKAL1 CDK5
regulatory
subunit
associated
protein 1-like 1
Presumed regulator
of cyclin kinase
611259 1.0-
1.20
0.31
7p15 JAZF1 Juxtaposed with
another zinc
finger gene 1
Zinc-finger protein
of unknown
function
606246 1.10 0.50
8q24.11 SLC30A8 ZNT8 Zinc transporter 8 611145 1.18 0.65
9p21 CDKN2A
CDKN2B
p16(INK4a)p14
(ARF)p15
9INK4b)
Cyclin-dependent
kinase inhibitor 2A
and 2B
600431 1.20 0.83
10p13-p14 CDC123

CAMK1D
Cell division
cycle protein
123 homolog

Calcium/calmod
ulin-dependent
protein kinase i-
delta
Required for S
phase entry of the
cell cycle

Mediator of
chemokine signal
transduction in
granulocytes
-

607957
1.11 0.18
10q23-q25 IDE

HHEX

KIF11
Insulin
degrading
enzyme

Hematopoietical
ly expressed
homeobox;
PRHX
Kinesin family
member 11;
Homologue of
Xenopus EG-5
Neutral
metallopeptidease
that can degrade
many peptides

Homeobox
transcription factor

Kinesin related
motor in
microtubule &
spindle function
146680

604420

148760
1.13 0.53
10q25.3 TCF7L2 TCF4 High mobility group
transcription factor
602228 1.31-
1.71
0.26
15

Table 2: Continued – Genetic Loci Implicated in ‘Common Variety” Type 2
Diabetes
Chromosome
Location
Gene
Symbol
Common/Other
Name
Cellular Function OMIM
Number
OR Frequency
of Risk
Allele
11p15.1 KCNJ11 Kir6.2 Inwardly rectifying
potassium channel
600937 1.14 0.47
12q21 TSPAN8
LGR5
Tetraspanin 8
Leucine-rich
repeat-
containing G
protein-coupled
Cell surface
glycoprotein
Orphan G protein
receptor
600769
606667
1.09 0.27
16q12.2 FTO Fat-mass- and
Obesity-
Associated Gene
58kD protein with
nuclear localization
signal
610966 1.27 0.38
OR=Odds ratio of type 2 diabetes per risk allele600769

2.9.1 PPAR-gamma
The screening of one single gene for SNPs also represents a huge and expensive
undertaking. The PPAR-gamma gene on the short arm of chromosome 3 spans 83,000
nucleotides with 231 SNPs in public databases, and seven of which are coding SNPs.
The gene codes for a nuclear receptor, which is predominantly expressed in adipose
tissue where it regulates transcription of genes involved in adipogenesis. In the 5’
untranslated end of the gene is an extra exon B that contains a SNP changing a proline in
position 12 of the protein to alanine. The rare Ala allele is seen in about 15% of
Europeans and was shown to be associated with increased transcriptional activity,
increased insulin sensitivity and protection against type 2 diabetes in an initial study
[Deeb et al. 1998]. Subsequently, there were a number of studies, which could not
replicate the initial finding. An analysis of parent-offspring trios showed excess
transmission of the Pro allele to affected offspring and a meta-analysis combining the
results from all published studies a highly significant association with type 2 diabetes
16

[Altshuler et al. 2000]. In fact, the Pro12Ala polymorphism is now one of the best-
replicated genes for type 2 diabetes (p<2 ×10
-10
). The individual risk reduction
conferred by the Ala allele is moderate, about 15%, but since the risk allele Pro is so
common, it translates into a population attributable-risk of 25% [Altshuler et al. 2000].

2.9.2 KCNJ11
The ATP-sensitive potassium channel Kir 6.2 (KCNJ11) forms an octamer protein that
regulates transmembrane potential and thereby glucose-stimulated in inuslin secretion in
pancreatic beta-cells together with the sulfonyurea receptor SUR1 (ABCC8). Closure of
the K-channel is a prerequisite for insulin secretion. A Glu23Lys polymorphism (E23K)
in KCNJ11 has been associated with type 2 diabetes and a modest impairment of insulin
secretion [Gloyn et al. 2003, Florez et al. 2004]. In addition, an activating mutation in
the gene causes a severe form of neonatal diabetes [Gloyn et al. 2004]. Whereas these
neonatal mutations result in a 10-fold activation of the ATP-dependent potassium channel,
the E23K variant results in only a 2-fold increase in activity [Nichols et al. 2002].
KCNQ1 encodes another potassium channel that has recently been implicated in type 2
diabetes by GWAS [Yasuda et al. 2008, Unoki et al. 2008].

2.9.3 WSF-1
Recently 1,536 SNPs in 84 candidate genes were studied for association with type 2
diabetes [Sandhu et al. 2007]. Only one of these genes was associated with type 2
diabetes; WSF1. The result was then replicated in 9,533 cases and 11,389 controls.
17

WSF1 encodes wolframin, a protein that is defective in individuals with the Wolfram
syndrome. This syndrome is characterized by diabetes insipidus, juvenile diabetes, optic
atrophy and deafness. Thereby WSF-1 can be considered the fourth candidate gene for
type 2 diabetes. The study also highlights some of the difficulties of candidate gene
studies. We are limited by our own imagination and only one out of 84 candidate genes
gave a positive result.

2.9.4 TCF7L2 (10q25)
One of the strongest associations in all of the studies is with region on chromosome
10q25 located in a 92 kb linkage disequilibrium (LD) block in the TCH7L2 gene. While
the identification of this type 2 diabetes locus predates the GWAS era [Grant et al. 2006],
the association of the TCF7L2 locus with type 2 diabetes is by far the strongest and most
consistent signal across with GWAS studies. In a meta-analysis of the WTCCC, Fusion,
and DGI studies, the combined odds ratio of type 2 diabetes per copy of the “ high-risk”
allele is 1.37 (95% confidence interval, 1.31-1.43), with a combined p-vale of 10
-48

[Zeggini et al. 2007, Scott et al. 2007, Saxena et al. 2007]. Individuals homozygous for
the high-risk allele have about a doubling of diabetes risk, or to put it another way, about
~12% of them will have type 2 diabetes versus ~9% of heterozygotes and ~6% of non-
carriers. The predisposing effect seems to be more pronounced among lead then obese
individuals [Cauchi et al. 2008, Watanabe et al. 2007].
TCF7L2, also know as TCF-4 or beta-catenin interacting protein, is a high-mobility
group box-containing transcription factor that is involved in the WNT signaling pathway,
18

acting as a nuclear receptor for beta-catenin [Yi et al. 2005, Prunier et al. 2004]. WNT
signaling critical for cell proliferation involved in many aspects of embryogenesis,
including adipogenesis [Prestwich et al. 2007], myogensis [Cossu et al. 1999], and
pancreatic islet development [McLin et al. 2007]. TCF7L2 activation induces a variety
of genes, including those for intestinal proglucagon and glucagon-like peptides-1 and -2
[Fehmann et al. 1995]. The association with type 2 diabetes involves a common
haplotype spanning part of intron 3, exon 4, and part of intron 4, but does not include any
coding variants, suggesting an effect on gene expression as the most likely explanation
for association with type 2 diabetes.
Clinically, carriers of the high-risk TCF7L2 genotype have reduced insulin secretion
[Florez et al. 2006], suggesting a possible role for TCF7L2 in the beta cell dysfunction of
type 2 diabetes. Over-expression studies in beta cell, however, have given conflicting
results, in one case showing a blunting of glucose-stimulated insulin secretion [Lyssenko
et al. 2007] and in another a beneficial effect to protect islets from glucose- and cytokine-
induced apoptosis and impaired function [Shu et al. 2008]. Other studies suggest roles
for TCF7L2 in type 2 diabetes via control of the incretin axis, hepatic glucose production,
and adipocyte function [Lyssenko et al. 2007, Cauchi et al. 2008]. Actual expression data
for TCF7L2 in humans are limited. One report indicates that islets from carriers of the
risk genotypes have increased TCF7L2 mRNA compared to non-carriers [Lyssenko et al.
2007], but no significant difference in TCF7L2 expression was observed in islets or
muscle in a study of unselected human type 2 diabetes in the Diabetes Genome Anatomy
Project (DGAP) [http://www.diabetesgenome.org]. TCF7L2 could exert its effect in a
19

variety of ways, since TCF7L2 is expressed at high levels in a wide variety of tissues,
including the hypothalamus.
2.9.5 SLC30A8 (8q24)
Another strongest SNP after those in TCF7L2 is in a 33kb LD block within the coding
region of SLC30A8, a zinc membrance transporter (Zn-T8) that is highly expressed in
pancreatic islets [Chimieti et al. 2005]. This gene first emerged as a type 2 diabetes locus
in the McGill/Imperial study [Sladek et al. 2007]. Although this polymorphism ranked
only 34
th
in the screening GWAS set with ORs of 1.18 and 1.53 for heterozygotes and
homozygotes, respectively, this was one of only a handful of SNPs for which the
association with type 2 diabetes was confirmed in the replication sets. Replication was
also obtained in the Decode, WTCCC, Fusion, and DGI studies [Zeggini et al. 2007,
Scott et al. 2007, Saxena et al. 2007, Steinthorsdottir et al. 2007].
Of all of the new potential type 2 diabetes genes, SLC30A8 is one of the new involving a
nonsynonymous polymorphism – an arginine to tryptophan substitution at amino acid
325 [Saldek et al. 2007]. SLC30A8-overexpressing cells display enhanced glucose-
stimulated insulin secretion [Chimienti et al. 2006], suggesting that the risk allele might
act by impairing transporter function, thereby decreasing the amount of zinc available for
co-crystallization with insulin in the secretory vesicles of beta cells. Reduced pancreatic
and islet zinc levels have been observed in some animal models of type 2 diabetes
[Taylor et al. 2005], and zinc supplementation has been shown to improve glucose
tolerance in db/db mice [Simon et al. 2001]. On the other hand, studies have not
identified any deficiency of pancreatic zinc content in patients with type 2 diabetes
20

[Taylor et al. 2005], and at least one study suggests that dietary zinc supplementation in
humans with diabetes further decreased glucose tolerance [Raz et al. 1989]. Since zinc
has many roles in the cell in addition to its role in insulin crystallization, and since Zn-T8
is only member of a large family of zinc transporters, more mechanistic studies are
clearly warranted. SLC30A8 has also recently been identified as an autoantigen in
human type 1 diabetes [Wenzlau et al. 2007].

2.9.6 HHEX/IDE/KIF11 (10q23)
A locus on 10q23 ranked third for association with type 2 diabetes (after TCG7L2 and
SLC30A8) in the McGill scan, and was independently replicated in the WTCCC, Fusion,
and DGI studies giving a small p-value (p=5.7×10
-10
), but a combined OR of only 1.13.
SNPs at this locus have been also found to be associated with measures of insulin
secretion in a large multicentric study from Europe and in the DPP study (Pascoe et al.
2007, Moore et al. in press). The association signal lies in a 295kb block of LD that
includes at least three potential type 2 diabetes genes: HHEX (a homeobox transcription
factor), KIF11 9a kinesin interacting factor), and IDE (insulin degrading enzyme) genes.
However, almost all of these genes showed rather broad tissue expression. Based on its
provocative name and data suggesting roles in both insulin signaling and islet function
[Farris et al. 2003], IDE would probably be viewed as the strongest biological candidate
for type 2 diabetes was negative [Florez et al. 2006]. Reanalysis of the DGAP data
reveals no difference in expression of IDE in islets between the individuals with diabetes
and healthy controls, while in muscle, expression of IDE is modestly reduced in type 2
21

diabetes (21% decreased, p=0.05). Despite its name, IDE is not an insulin-specific
protease and has been shown to play roles in degradation of glucagon, brain amyloid
proteins, and viral glycoproteins [Qiu et al. 1998]. HHEX, on the other hand, is a
transcriptional repressor, active in cardiac and pancreas development [Tanaka et al. 1999,
Foley et al. 2005], as well as WNT signaling [McLin et al. 2007], while KIF11 is
involved in centrosome migration and mitosis [Kapitein et al. 2005].

2.9.7 CDKAL1 (6p22)
An association signal at 6p22 maps to a 15 kb linkage disequilibrium block in intron 5 of
the gene for CDKAL1 (CDK5 regulatory subunit-association protein 1-like-1). The
strongest association of 6p22 with type 2 diabetes was observed in the Decode study
(OR-1.20) [Steinthorsdottir et al. 2007], with weaker association in the WTCCC, Fusion,
and DGI study (combined OR=1.12), and no association in the McGill/Imperial study
[Zeggini et al. 2007, Scott et al. 2007, Saxena et al. 2007, Sladek et al. 2007].
The CDKAL1 gene encodes a 65 KD protein that is expressed in a broad range of tissues
and is believed to be an inhibitor of CDK5 (cyclin-dependent kinase 5). CDK5 islet has
been shown to blunt insulin secretion in response to glucose and to play a permissive role
in the decrease of insulin gene expression that results from glucotoxivity [Ubeda et al.
2006]. Thus, once can speculate that reduced expression of CDKAL1 would results in
enhanced activity of CDK5 in beta cells, and this would lead to decreased insulin
secretion; in agreement, this locus was significantly associated with small decreases in
insulin response to a glucose load [Saxena et al. 2007, Sladek et al. 2007, Pascoe et al.
22

2007, Palmer et al. 2008, Stancakova et al. 2008]. However, in the analysis of human
islet and muscle expression data from DGAP, there does not appear to be a significant
difference in the level of expression of CDKAL1 between subjects with diabetes and
controls.

2.9.8 CDKN2A/2B (9p21)
Two signals of association with type 2 diabetes have been localized to chromosome 9p21.
The first one – represented by SNP rs10811661 – is supported by DGI, WTCCC, and
Fusion cans with a combined OR of 1.20 and a p=7.8×10
-15
[Zeggini et al. 2007, Scott et
al. 2007, Saxena et al. 2007]. A second weaker signal in this region was detected by the
WTCCC and Fusion studies about 100 kb in a telomeric direction (SNP rs564398,
p=1.3×10
-6
and p=0.039, respectively). The two association signals, involving noncoding
polymorphisms, are separated by a recombination hotspot and thus could be independent.
The centromeric signal (SNP rs10811661) is close to the CDKN2A and CDKN2B genes
(cyclin-dependent kinase inhibitor 2a and 2b) and the telomeric signal includes both of
these genes and these two genes code for inhibitors of CDK4 (cyclin-dependent kinase 4).
CDK2A encodes two molecules, p16
INK4a
and p14 (ARF), while CDK2B encodes
p15
INK4b
[Kamb et al. 1994, Pomerantz et al. 1998].
CDK4 is involved in cell-cycle regulation in a wide variety of cells. Interestingly, mice
with targeted disruption of this gene have small islets and develop insulin-deficient
diabetes, while mice expressing a CDK4 from insensitive to physiological inhibitors
exhibit beta cell hyperplasia [Rane et al. 1999]. However, a study of 1,276 healthy
23

individuals failed to observe any association between CDKN2A or CDKN2B
polymorphism and insulin secretion [Pascoe et al. 2007]. These two genes, however, are
widely expressed and could have effects in many other tissues. Furthermore, this region
includes other genes, including a mitochondrial RNA processing endoribonuclease and
the gene for methylthioadenosine phosphorylase [Nobori et al. 1996], as well as several
ESTs. Interestingly, a “major” locus for coronary artery disease, abdominal aneurysms,
and peripheral vascular disease has also been identified in this region [Helgadottir et al.
2007, McPherson et al. 2007, Helgadottir et al, 2008], suggesting a common genetic link
between diabetes and vascular disorders [Stern et al. 1995].

2.9.9 IGF2BP2 (3q27)
Association signals at 3q27 were observed in the WTCCC, Fusion, and DGI scans,
although the combined OR is only 1.14 (p=8.9×10
-16
) [Zeggini et al. 2007, Scott et al.
2007, Saxena et al. 2007]. This is consistent with previous studies showing linkage at
this location with quantitative metabolic traits and type 2 diabetes [Kissebah et al. 2000,
Vionnet et al. 2000]. The SNPs displaying the strongest association with type 2 diabetes
lie in a 50 kb region within intron 2 in the gene coding for IGF-2BP2. IGF-2BP2 is not
an IGF binding protein, but a protein that binds to the 5’UTR of the insulin-like growth
factor 2 (IGF-2) mRNA, thereby regulating its translation [Nielsen et al. 1999]. Several
other genes with important metabolic functions are also within the radius of a potentially
regulatory effect by the risk variants, including PP1R2 (protein phosphatase 1, regulatory
subunit 2), MAP3K13 (mitogen-activated protein kinase), LIPH (lipase H), DGKG
24

(diacylgycerol kinase gamma 1), AHSG 9alpha-2-HS-gylcoprotein, a putative inhibitor
of insulin receptor signaling), and ADI/POQ, the insulin-sensitivizing adipokine
adiponectin. Reanalysis of the DGAP human islet and muscle data shows no difference
in expression of IGF2BP2 in diabetes is mediated by an effect on the expression of
IGF2BP2 or these other genes will have to be addressed by further studies.

2.9.10 FTO (16q12)
In the WTCCC scan, the association signal at this signal at this locus (OR=1.27,
p=7.3×10
-14
) was second in magnitude only to that of TCF7L2 [Zeggini et al. 2007]. The
diabetes-associated alleles at this location, however, were also strongly associated with
increased BMI, and the association with type 2 diabetes was lost when the analysis was
adjusted for body weight, suggesting that the effect on type 2 diabetes risk is mediated by
an effect on adiposity. Indeed, this locus has been associated with obesity, with ORs
ranging from 1.3 to 1.9 [Frayling et al. 2007, Dina et al. 2007]. Adults homozygous for
the high-risk A allele, and these individuals are 1.7 times more likely to be obese than
those homozygous for the low-risk T allele. The SNPs showing the strongest association
lie in a 47 kb linkage disequilibrium block encompassing parts of the first two introns and
exon 2 of the FTO gene.
The function of the FTO (fat and obesity associated) gene is still unclear. FTO shares
sequence motifs with Fe(II)- and 2-oxo-glutarate-dependent oxygenases and is localized
in the nucleus, where it catalyzes the demethylation of 3-methylthmine in single-strand
DNA [Gerken et al. 2007]. Other genes in close proximity to the FTO polymorphism
25

include an Akt interacting protein (AKTIP), two members of the RPGRIP1L (a gene of
known function). Functional studies based on knockout and over-expression models will
be needed to understand the pathways through which variants at this locus control body
weight and glucose homeostasis.

2.9.11 KCNJ11 and PPAR-gamma
Most of the genes previously identified through the candidate gene approach did not rank
high for association with type 2 diabetes in the GWAS studies. KCNJ11 and PPARG are
two exceptions. Both were found to be associated with type 2 diabetes in the WTCCC,
Fusion, and DGI studies a combined OR of 1.14 (p=6.7×10
-11
and p=1.7×10
-6
,
respectively) [Zeggini et al. 2007, Scott et al. 2007, Saxena et al. 2007], ad both involve
nonsynonymous polymorphisms. The association with the KCNJ11 gene concerns a
common glutamate to lysine substitution at position 23 (E23K). The lysine allele has
been shown to reduce the sensitivity of beta cell ATP-sensitive K
+
channels toward
inhibitory ATP
4-
, thereby increasing the threshold for insulin release [Schwanstecher et al.
2002]. Consistent with this, this polymorphism has been associated with an insulin
secretion defect in multiple studies [Florez et al. 2004, Nielsen et al. 2007]. However, the
beta cell may not be the only important target organ for KVNJ11. A transgenic mouse
expressing a mutant Kir6.2 subunit (encoded by the KCNJ11 gene) in the hypothalamus
driven by the POMC promoter exhibits impaired whole-body glucose disposal and a
defect in diet [Beamer et al. 1998]. Thus, the KCNJ11 polymorphisms could contribute
26

to loss of glucose sensing in both the beta cell and POMC neurons, leading to impairment
in insulin action as well as insulin secretion.
The identified polymorphism in PPAR-gamma, which appears to modify susceptibility to
type 2 diabetes and obesity, was identified a decade ago and produces a Pro12Ala change
in the PPAR-gamma2 gene [Deeb et al. 1998, Beamer et al. 1998]. This was initially
confirmed in a meta-analysis [Altshuler et al. 2000] and reconfirmed in the GWAS
studies. Resistance to diabetes is associated with the minor (Ala12) allele and
susceptibility with the major allele (Pro12), which has a prevalence of about 85% among
non-diabetic individuals and 88% among diabetic subjects. Exactly how this change in
amino acid produces this effect still remains unclear. However, this change occurs
specifically in the PPAR-gamma2 isoform of the gene, which is the form specifically
expressed in adipose tissue and which is the target of the insulin sensitizing
thiazoilidinediones (TZDs). Mutations in other functional regions of this gene have also
been found in individuals with severe insulin resistance due to familial partial
lipodystrophy type 3 [Zeggini et al. 2008].

2.9.12 JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9,
and NOTCH2
Genomic loci near these six genes did not reach significance in the original GWAS
screens, but their association with type 2 diabetes was confirmed in a large replication
study including more than 50,000 individuals, with OR ranging from 1.09 to 1.15 [Graup
et al. 2008]. JAZF1, CDC123/CANK1D, and TSPAN8-LGR5 are associated with small
27

alterations in insulin secretion, whereas the mechanisms linking the other three loci to
type 2 diabetes remain uncertain [O’Rahilly et al. 2007].
2.10 Additional Loci Associated with Quantitative Metabolic Traits
Mutations in the melanocortin-4 receptor (MC4R) have previously been shown to
account for up to 5% of childhood-onset severe obesity [Loos et al. 2008], and analysis of
the GWAS data has shown a modest, but consistent, association of fat mass and waist
circumference with a locus immediately down-stream of this gene [Chambers et al. 2008,
Chen et al. 2004]. Analysis of quantitative traits among the non-diabetic controls of type
2 diabetes GWAS studies has also identified variants in the region of the glucose-6-
phosphatase catalytic subunit 2 (G6PC2) that function as modulators of fasting glucose,
although this effect does not appear to translate into an increased risk of type 2 diabetes
[Diabetes genetic initiative 2007]. No consistent associations have been thus far reported
with measures of insulin secretion and insulin sensitivity, but further insights are
expected from the meta-analyses of the available GWAS data in the future.

2.11 Whole Genome Association Studies
Given the limited success with linkage studies and candidate gene studies researchers
turned their hope to new tools. The rapid improvement in high throughput technology for
SNP genotyping decreasing costs per genotype (in 10 years the cost has decreased by a
factor of 10) has opened new possibilities for both linkage and association studies. The
Hap map provided another important tool showing that genotyping approximately
28

500,000 SNPs in the entire genome would cover about 75% of all common variants in the
genome. Last 3 years brought about a real breakthrough in the genetics of type 2 diabetes.
The reason was that several so called whole genome association studies (WGAS) using
DNA chips with more than 500,000 SNPs in a large number of patients with type 2
diabetes and controls have been performed and published [Zeggini et al. 2007, Scott et al.
2007, Sladek et al. 2007, Steinthorsdottir et al. 2007, Pascoe et al. 2007]. Robust
statistical evidence of association in these stduies requires p-values lower than 5 × 10
-8
.
In a collaborative study with the Broad Institute and Novartis (Diabetes Genetic Initiative,
DGI) they performed a QWAS in 1,464 patients with type 2 diabetes and 1,467 non-
diabetic control subjects from Finland and Sweden. When compared the only positive
results with FUSION (Finnish USA study of NIDDM) and WTCCC (Welcome Trust
Case Control Consortium) groups [Zeggini et al. 2007]; these positive results were seen
and replicated in all three studies based upon DNA from 32,000 individuals.
The transcription factor gene TCF7L2 was on top of the first list of each WGAS with a
joint p-vale in the tree scans of 10
-50
. In addition to TCF7L2, PPAR-gamma, KCNJ11
and WSF1 the first WGAS studies have identified at least six novel/loci for type 2
diabetes: CDKN2A and CDKN2B encoding the tumor suppressor cyclin-dependent
kinase inhibitor-2A and -2B which may play a role in pancreatic islet regenerative
capacity through inhibition of CDK4 and CDK6 (cyclin-dependent kniase 4 and 60,
respectively; IGF2BP2 which encodes insulin-like growth factor 2 binding protein 2, an
mRNA binding protein implicated in transport of RNA targets to enable protein synthesis
important for pancreas development as judged by studies in Xenopus and transgenic
29

mice; CDKAL1 which is homologous to CDK5RAP1, an inhibitor of cyclin-dependent
kinase CDK5 which transduces glucotoxicity signals in pancreatic beta-cells; SLC30A8
(solute carrier family 30, member 8) encoding a pancreatic beta-cell specific zinc
transporter which may affect insulin stability, storage, or secretion; and the HHEX
(hematopoietically expressed hemeobox) region which also harbors IDE encoding
insulin-degrading enzyme which has been implicated in both insulin signal and islet
function. Both HHEX (critical for ventral pancreas development), and IDE are powerful
biological candidates for type 2 diabetes.
A central theme for many of the recently discovered genes is that many of them seem to
be involved in insulin secretion, pin-pointing the pivotal role of beta-cell (dys)function in
the pathogenesis of type 2 diabetes. These genes include TCF7L2, KCNJ11, HHEX,
SLC30A8, CDKAL1, CDKN2A/2B, IGF2BP2 and KCNQ1 [Yasuda et al. 2008, Unoki
et al. 2008, Scott et al. 2007, Steinthorsdottir et al. 2007, Staiger et al. 2008, Staiger et al.
2007, The Wellcome Trust Case Control Consortium 2007]. The loci seem particularly
to be associated with an increased risk of developing type 2 diabetes through a reduced
insulin-secretory capacity. It could be assumed that these type 2 diabetes gens only
represent the tip of the iceberg, and more refined analyses will certainly yield additional
genes associated with type 2 diabetes. Recent analyses have also implicated CDC123
(cell division cycle 123 homolog) and CAMK1D (calcium/calmodulin-dependent protein
kinase ID) [Graup et al. 2008]. CDC123 is regulated by nutrient availability in S.
cerevisiae and has a role in cell cycle regulation. Taken together, evidence from GWAS
implicating variants in or near CDKAL1, CDKN2A/B, CSC123 and CAMK1D suggests
30

that cell cycle dysregulation may be a common pathogenetic mechanism in type 2
diabetes. Several of the new genes seem to influence cell proliferation by interfering
with the cell cycle as e.g. CDKAL1 and CDKN2A/CDKN2B in chromosome 9.
Intriguingly, the same region on chromosome 9 showing association with type 2 diabetes
was associated with increased risk of myocardial infraction in three independent WGAS
[Helgadottir et al. 2007, McPherson et al. 2007, Thomas et al. 2008]. However, different
SNPs are most likely operative for type 2 diabetes and myocardial infraction.
Further recent meta-analysis efforts such as DIAGRAM have identified 6 further
candidate SNPs [Yasuda et al. 2008, Unoki et al. 2008, Graup et al. 2008, O’Rahilly et al.
2007]: JAZF1 (juxtaposed with another zinc finger gene 1) encodes a transcriptional
repressor of NR2C2 (nuclear receptor subfamily 2, group C, member 2) 16. Mice
deficient in Nr2c2 show growth retardation, low IGF1 serum concentrations and perinatal
and early postnatal hypothglycaemia; CDC123 and CAMK1D ; TSPAN8 (tetraspanin 8)
which encodes a cell-surface glycoprotein expressed in carcinomas of colon, liver and
pancreas; THADA (thyroid adenoma associated) gene; ADAMTS9 (ADAM
metallopeptidase with thrombospondin type 1 motif, 9), encoding a widely expressed and
secreted metalloprotease that cleaves the proteoplycans versican and aggrecan; NOTCH2
(Notch homolog 2 Drosophila) encoding a type 1 transmembrance receptor expressed in
embryonic ductal cells of branching pancreatic buds during pancreatic organogenesis in
mice and; ADAM30 (ADAM metallopeptidase domain 30) that represents the same
signal [Graup et al. 2008].
31

Curiously, some of the recent studies suggest a possible explanation for previous much
debated epidemiological observations that men with type 2 diabetes are less likely to
develop prostate cancer. The same allele in HNF1B (or TCF2) that predisposes to type 2
diabetes was protective of prostate cancer [Loos et al. 2008]. Moreover, different
variants in JAZF1 are associated with type 2 diabetes and with prostate cancer [Zeggini et
al. 2007, Scott et al. 2007, Pascoe et al. 2007, Frayling et al. 2008]. In keeping with an
effect on development and transcriptional processes these findings may not come as
surprise although the exact causal relationships remain to be investigated [Samaras et al.
1997].

2.12 Obesity Genes and Type 2 Diabetes
Obesity is clearly one of the driving risk factors behind developing type 2 diabetes.
However, not all subjects with diabetes are obese and not all obese develop diabetes.
With this scenario in mind it is important to consider whether a possible association
between a gene and type 2 diabetes is in fact due to a gene associated with obesity or vice
versa. FTO, the strongest identified obesity gene so far increases the risk of type 2
diabetes [Frayling et al. 2007, Dina et al. 2007]. It is therefore not surprising that FTO
was not detected as associated with type 2 diabetes in the WGAS matched for BMI.
Although insulin resistance is an early detectable defect in subjects at increased risk of
developing type 2 diabetes, obesity, and particularly abdominal obesity, usually precedes
insulin resistance in these individuals. About 40% of the variation in body fat can be
attributed to genetic factors [Bouchard et al. 1988]. The genetic influence is even more
32

impressing for abdominal obesity. For example, genes are considered to explain 60% of
the variance in abdominal fat in postmenopausal women [Carey et al. 1996, Zhang et al.
1994]. Abdominal fat tissue could provide as signal for the chain of events leading to
skeletal muscle insulin resistance. Two such candidate signals ate the adipocyte
hormones leptin, and another tumor necrosis factor-alpha (TNF-alpha) [Hotamisligil et al.
1993, Pelleymounter wt al. 1995]. A mutation in the leptin gene results in complete
absence of the protein in the fat ob/ob mouse. Treatment of the ob/ob mouse with leptin
resulted in marked weight loss [Montague et al. 1997]. This is another example of a
monogenic disorder with a similar although exaggerated phenotype as found in common
obesity. Two morbidly obese children from consanguineous parents had very low
circulating leptin levels due to a frame shift mutation involving a deletion of a singled
guanine nucleotide in the leptin gene [Considine et al. 1995]. Whereas some progress in
treatment of these children with leptin has been reported, treatment of obese subjects
without mutations the leptin gene has been less successful. Obese humans actually have
elevated rather decreased levels of leptin suggestive of hormonal resistance analogous to
insulin resistance [Maffei et al. 1995] and leptin levels show a strong positive correlation
with the total fat mass [Yeo et al. 1998]. Moreover, contribution of common variants in
the leptin receptor gene to obesity and obesity-related phenotypes including leptin levels
has remained more or less elusive. Other monogenic forms of obesity implicate the gene
encoding the melanocortin 4 receptor (MC4R) [Frayling et al. 2007, 1Krude et al. 1998]
which serves as the hypothalamic receptor for the anorexigenic peptide a-MSH
(melanocyte stimulating hormone). Alpha-MAH is derived from the product of pro-
33

opiomelanocortin (POMC), which has also been associated with monogenic forms of
(early-onset) obesity, either directly or indirectly [Jackson et al. 1997, Larsen et al. 2005].
MC4R gene variants are expected to account for as much as 5% of severe obesity cases
[Rankinen et al. 2006]. Recently, variants close to MC4R were found to associate with
obesity in a large study involving over 90,000 samples thus establishing MC4R as the
second replicated obesity gene after FTO [Chambers et al. 2008].
The list of candidate genes for both monogenic and particularly for common obesity that
has been complied through numerous one-by-one candidate gene association studies is
very long [Herbet et al. 2006]. Unfortunately, many of these studies are underpowered
and the filed probably plagued by publication bias. With the advent of GWAS this has
now changed although progress has been slower for obesity than for type 2 diabetes.
Thus far two genes (FTO and MC4R) have been identified in two different scans but
several more scans including a substantial number of subjects is under way or already in
press. Insulin-induced gene 2 (INSIG2) was identified in a low-density scan in the
Farmingham cohort where heritability estimates for BMI range between 37 and 54%
[Lyon et al. 2007]. Out of 116,204 SNPs tested in 694 participants only one SNP reached
overall significance. The obesity-associated INSIG2 genotype was present in 10% of
individuals, homozygotes being about one BMI unit heavier than heterozygotes or non-
carriers, regardless of sex and age. The protein encoded by INSIG2 is also a functionally
attractive candidate gene for obesity since it inhabits the synthesis of fatty acids and
cholesterol. Loss of function may thus need excessive storage of surplus lipid in the form
of adipose tissue. The association between INSIG2 and obesity has been replicated in
34

some but certainly not all populations tested including meta-analysis of obesity WGAS
[Lyon et al. 2007, Jackson et al. 2008]. A stronger candidate was then found in FTO
[Frayling et al. 2007]. As mentioned above, the gene predisposes to diabetes through an
effect on BMI with a 3 kg between-homozygote difference reflecting fat mass [Frayling
et al. 2007]. The obesity-associated FTO risk allele is present in 16% of adults resulting
in an increased risk for obesity with about 30% for one and 70% for two copies [Frayling
et al. 2007]. The function of the gene product of FTO (fat mass and obesity associated) is
largely unknown. A recent bioinformatics analysis suggests that it may serve as 1 2-
oxoglutarate-dependent nucleic acid demethylation and fat mass remains to be elucidated.
FTO genotypes seem to affect metabolic variables in line with the effect on BMI.
However, we have also found association between FTO and adolescents that appear to be
independent of BMI [Moota et al. 2003].

2.13 Genetic Influences in Mitochondrial Dysfunction
Since genes are transcribed to RNA, RNA translated into proteins, and defects in proteins
cause disease the ultimate goal would be to carry out a random search of expressed
proteins in target tissues. This may not yet be completely feasible but the study of large-
scale transcript profiles is. This approach has been successful in defining prognosis of
cancers but for complex diseases affecting many target tissues it may not be that simple.
Moreover, defining what is differentially expressed among more than 20,000 gene
transcripts on a chip is a statistical challenge. Despite these problems analysis of gene-
expression in skeletal muscle of patients with type 2 diabetes and pre-diabetic individuals
35

has provided new insights into the pathogenesis of the disease applying pathway analysis
rather then analysis of expression of single genes. This based upon the assumption that if
one member of the pathway shows altered expression, this will be translated into the
whole pathway [Patti et al. 2003]. Transcription analysis has shown that genes regulating
oxidative phosphorylation on mitochondria exhibit a 20% coordinated down-regulation in
skeletal muscle from pre-diabetic and diabetic individuals compared with non-diabetic
controls [Patti et al. 2003, Luan et al. 2001]. A similar down-regulation of the gene
encoding a master regulator of oxidative phosphorylation, the PPAR-gamma co-activator
PGC-1alpha was also observed. Thus OXPHOS genes, has emerged as central in the
pathogenesis of type 2 diabetes suggesting that impaired mitochondrial function and
impaired oxidation of fat may predispose to type 2 diabetes through a “thrifty gene”
mechanism. By studying young and elderly twins we could demonstrate that elderly
carriers of a Gly482Ser polymorphism in the PGC-1alpha gene had decreased expression
of the PGC-1alpha in skeletal muscle, suggesting that genetic variants determine the age-
related decline in expression of key genes regulating oxidative phosphorylation [Ling et
al. 2004]. This study gives an example of how genetic factors, in combination with non-
genetic factors, can influence gene-expression, which subsequently affects glucose and
fat metabolism. The interaction between genetic and non-genetic factors may be even
more complex and involve epigenetic factors such as DNA methylation and histone
modifications. So far, the knowledge of the influence of epigenetic factors on the
pathogenesis of type 2 diabetes remains limited.

36

2.14 Gene-Environment Interaction
The rapid increase in type 2 diabetes during the past 50 years must be ascribed to changes
in the environment rather than to genes, as the genetic background has not changed
during this period. But the genetic background determines how we respond to the
environment. The PPAR-gamma receptor is a good example of an interaction between
genes and the environment. PPAR-gamma activators have become a major new type of
anti-diabetic drugs (thiazolidinediones) while dietary long-chain polyunsaturated fatty
acids are supposedly natural ligands for PPAR-gamma. The importance of the genetic
variation in PPAR-gamma as a significant modulator of physiological responses to
dietary fat in humans has been demonstrated in several studies [Memisoglu et al. 2003,
Ylonen et al. 2008, Bruning et al. 1997]. The different genotype carriers show different
association between intake of total fat, fat subtypes, and obesity. There are also data to
suggest that the protective effect of the Ala allele is influenced by the degree of saturation
of ingested fat [Memisoglu et al. 2003, Ylonen et al. 2008, Bruning et al. 1997]. This
may not be too surprising since free fatty acids have been proposed as natural ligands for
PPAR-gamma.

2.15 The Emerging Genetic Architecture of Type 2 Diabetes
Taken together, these GWAS studies may suggest about the genetics of type 2 diabetes as
the following. First, these studies suggest that type 2 diabetes may be more
heterogeneous and more polygenic than previously believed. While previous reviews had
37

suggested there may be two or three major genetic forms of type 2 diabetes along with
some minor forms, the GWAS studies would suggest that from a genetic perspective type
2 diabetes represents a very large number of different disorders. This seems a bit at odds
with the common clinical presentation of the disease and raises a question as to how these
different genetic forms of type 2 diabetes might differ at a clinical level.
The second finding of these studies is the relatively high frequency of each of the risk
variants in the population, ranging from 0.26 for TCF7L2 to 0.85 for PPAR-gamma
(Figure 8-1). The high frequency of the risk variants together with the small risk of type
2 diabetes conferred by each allele variant translate into a very large number of
individuals who carry several of the disease variants but do not develop type 2 diabetes.
Indeed, one can estimate from the individual allele frequencies that 99% of the
population carries nine or more risk alleles, whereas only 8-10% of the population
develops type 2 diabetes. Such low penetrance is consistent with the mild effect of the
risk variants, requiring the presence of other factors, presumably environmental, for the
development of hyperglycemia. The mild effect may also be explained by the fact that
most of these variants are in non-coding regions of the gene, where they may produce
only subtle differences in regulation of expression. This scenario contrasts sharply with
that of Mendelian forms of diabetes, such as MODY, which are caused by rare mutations
in the coding sequence resulting in significant amino acid substitutions or truncated
proteins, leading to hyperglycemia even in the absence of other diabetogenic exposures.
The third finding is that the loci identified to date appear to explain only a small
proportion of the familial clustering of type 2 diabetes. Indeed, in the WTCCC study, the
38

nine strongest loci together account for only 7% of the 30-60% increase in the risk of
type 2 diabetes typically observed in siblings of type 2 diabetes probands as compared to
the general population [Zeggini et al. 2007]. This would suggest that the loci identified
to date are just the tip of iceberg. In each GWAS scan, other loci showed significant
associations with replicated across multiple studies. While some of these are certainly
false positives, some of these are likely genuine effects that need to be explored further.
At least four other hypotheses also need to be considered in interpreting these studies.
First, it is possible that the previous estimates of the contribution of genetics to
development of type 2 diabetes may be exaggerated the inability to assess other factors in
family and population studies, such as shared environment. Indeed, there are
environmental risk factors that may not have been previously appreciated, and compared
to our robust approaches to genetic analysis, the techniques for identifying environmental
risk factors are still very primitive. Furthermore, critical environmental exposures (e.g.,
maternal obesity, intrauterine environment, nutritional composition, or differences in
intestinal flora) may be subtle and begin as early as intrauterine life, thus altering patterns
of development and programming metabolic responsiveness to environmental stimuli
during later life. Such concepts are supported by studies of monozygotic twins, in which
primary DNA sequence is identical, yet diabetes risk is discordant and linked to birth
weight [Poulsen et al. 1999] and by the findings that low birth weight confers increased
risk diabetes equal in magnitude to the effect of the TCF7L2 polymorphism [Cauchi et al.
2008].
39

Second, our concepts about how genes interact with each other and with the environment
may be too simplistic. For example, in a polygenic rodent model of type 2 diabetes
created by heterozygous inactivation of both the insulin receptor and IRS-1, there is
evidence an produce diabetes in more than 10% of mice, but when they occur together,
more than 50% of animals develop diabetes at young age [Almind et al. 2002].
Furthermore, appearance of “clinical” diabetes in this and other rodent models can be
markedly different depending on the genetic background of the mouse, demonstrating
other important gene-gene interactions [Sharif et al. 2007].
Third, it is possible that the current models of genetics are masked by the underestimate
of the role of epigenetics in disease. In rodent models that mimic low birth weight in
humans by food restriction or restriction of placental flow of pregnant dams, it has now
been clearly demonstrated that not only do the low birth weight offspring have an
increased risk of diabetes, but this risk is passed on to the second generation, probably
through effects on intrauterine and early postnatal nutritional effects on genetic
imprinting and/or epigenetic regulation of gene expression or development [Ozanne et al.
2007, Kimenez-Chillaron et al. 2006, Park et al. 2008]. Moreover, these epigenetic
marks, while resulting from early life exposures, can actually progress during postnatal
life and thus contribute to age-dependent transcriptional regression of key metabolic
genes, as has recently been demonstrated for the beta cell transcription factor PDX1
[Raychaudhuri et al. 2008] and the insulin-sensitive glucose transporter GLUT4 [Woo et
al. 2008, Vavouri et al. 2006].
40

Finally, it needs to consider the possibility that the model of the genetics of type 2
diabetes with a moderate number of relatively common polymorphisms causing disease,
may be incorrect. If the genetic component of type 2 diabetes is represented by
uncommon (<5% of the population), but highly penetrant, disease alleles, the GWAS
studies conducted to date would have been limited in their ability to find the disease
genes since the current SNP arrays capture only a small fraction of all the rare variants
that are estimated to exist in the human genome.

2.16 Insights into Pathogenesis from GWAS Studies
The GWAS association studies continue to provide evidence to the broad nature of
molecules that might contribute to the pathogenesis of type 2 diabetes. Clearly many of
the genes identified in both GWAS and positional cloning studies would not be
considered typical candidate genes. However, it needs to be cautious about interpreting
what these studies tell us about the genes and tissues involved in the pathogenesis of type
2 diabetes. It is especially important to keep in mind that most of the observed
associations are in large domains of strong linkage disequilibrium in non-coding regions
of the genome. These association signals are usually named as if there are defects in the
closest specific gene, but many of these domains contain multiple genes, and thus the
“designated” type 2 diabetes gene may or may not be the gene whose expression or
function is altered by the polymorphism. Also, in silico analyses predict that up to 50%
of conserved cis-acting elements in the human genome up to 1 Mb from target genes,
sometimes in the introns of neighboring genes [Carroll et al. 2005]. These predictions
41

have been confirmed by chIP-on-chIP experiments [Birney et al. 2007] and RACE data
from the Encode project [Pearson et al. 2006]. Thus, the true type 2 diabetes genes may
be placed at some distance from the association signals.
With these caveats in mind, it is interesting to note that several of the genes placed in the
proximity of GWAS signals are expressed in beta cells. This, together with the findings
of association with glucose-stimulated insulin secretion [Florez et al. 2006, Sladek et al.
2007, Steinthorsdottir et al. 2007], has led some to concluded that beta cell defects have a
more primal role in the etiology of type 2 diabetes that insulin resistance or that changes
in muscle oxidative metabolic phenotypes are not genetic in origin. However, this may
not be necessarily the case. The focus of the GWAS studies on overt type 2 diabetes
favored finding genes marking a limitation of beta cell function, since ultimately all
forms of diabetes could be viewed as having relative insulin deficiency. The
identification of variants predisposing to diabetes through effects on insulin sensitivity
will require other study designs, such as ones based on association between insulin
sensitivity and genotype in the prediabetic state, i.e., before te onset of overt
hyperglycemia. Another factor masking the discovery of potential insulin resistance
genes in GWAS studies may be the very strong environment-gene interactions for insulin
resistance with body weight, physical activity, and other factors, as compared to the
potentially purer manifestation of genetic control on insulin secretion.

42

2.17 Pharmacogenetics
An important goal of genetics is to use information to improve treatment, i.e. to identify
individuals who are more likely than others to respond to a specific therapy. This has
been shown in neonatal diabetic patients with KCJN11 mutation [Pearson et al. 2003].
When the patients were switched from insulin to sulfonylurea their symptoms markedly
improved. This was especially dramatic for the severe neurological symptoms often
associated with the disease. Patients with MODY3 (HNF-1alpha mutations) are
supersensitive to treatment with sulfonylurea whereas they respond poorly to treatment
with meformin [Pearson et al. 2007]. It has also recently been shown that individuals
with the TCF7L2 risk genotype respond poorly to treatment with sulfonylureas,
eventually as a consequence of their more severe impairment in beta-cell function
[Lyssenko et al. 2005].

2.18 Genetic Prediction of Type 2 Diabetes
A few studies have tried to use genetic variants to predict future type 2 diabetes.
Lyssenko et al. showed that the Pro12Pro genotype in PPAR-gamma predicted future
type 2 diabetes in individuals with BMI > 30kg/m
2
and fasting plasma glucose > 5.5
mmol/l with an OR of 1.7 [Lyssenko et al. 2005]. The TT risk genotype of SNP 44 in
CAPN10 increased this risk in an additive manner to an OR of 2.7. Ridderstrale et al.
showed that risk genotype carriers of TCF7L2 had a 1.5-fold increased risk of developing
future type 2 diabetes [Ahlzen et al. 2008]. In keeping with this observation, Weedon et
43

al. showed that each risk allele of these three genes increased the OR by 1.28 yielding an
additive OR of 5.78 in a cross-sectional study [Weedon et al. 2006]. It can be expected
that cross-sectional studies will give higher risk estimates than prospective studies, as
they tend to include more severe cases of type 2 diabetes.

2.19 Future Directions
Dissecting the genetics of type 2 diabetes is still a complicated task but by no means the
nightmare task that it used to be. The last 10 years, particularly the last two, have
brought about tangible breakthroughs and we can now list at least eighteen genes that
have consistently been shown to increase the risk of type 2 diabetes. We are likely to see
a doubling of this number within the next one to two years. Meanwhile, these genes
explain only a small proportion (≈0.3) of the individual risk of type 2 diabetes (lamdas of
3). Although we now seem to cover approximately 75% of the genetic map of type 2
diabetes, the variants detected most likely represent “low hanging fruit” or common
variants. It is possible that there are more rare variants with stronger effects not detected
with the current available methodology. These variants are most likely seen in patients
with early-onset forms of diabetes or in individuals with a marked beta-cell dysfunction.
It is unlikely that genotyping using high-density DNA arrays can detect these rare
variants. Their detection will rather require sequencing. Sequencing of the whole
genome was once a dream, but with new technology this dream may become true in a
very near future. The role of copy number variations (CNVs) in the pathogenesis of
disease has been highlighted in the past years but the tools to detect these CNVs have
44

limited further exploration. This problem may be solved with the introduction of new
DNA chips with a much better coverage of CNVs. We are only beginning to realize that
epigenetic alteration (DNA methylation, histone acetylation and deacetylation) can
introduce epigenetic changes during life-time. Such changes may influence age-related
changes in gene-expression and thereby contribute to age-related diseases. Until now,
DNA methylation has been studied by laborious bisulfite sequencing of single genes. In
the future, the possibility of whole genome DNA methylation studies may shed new light
on the extent and importance of these epigenetic effects. Dissection of the genetic
complexity of type 2 diabetes and obesity may thus be possible after all. Besides these
issues that are mostly related to or depend on methodology the real task will be to probe
the interaction between genes, environment, and treatment and how to bring these results
back subjects who have developed type 2 diabetes are at risk of developing the disease.

45

Chapter 3: Paper One

Genetic Susceptibility to Type 2 Diabetes May Be Modified by Shared
Early Environment

3.1 Abstract
Background Genetic and environmental factors have been implicated in development of
type 2 diabetes. We assessed these factors using a population-based twin registry.
Methods A follow-up study of 92 monozygotic (MZ) and 198 dizygotic (DZ) pairs with
self-reported type 2 diabetes in at least one member was conducted. The twins were first
ascertained in 1992 from the California Twin Program (CTP), and the follow-up dates
were from January, 1997 to December, 2001. The concordance rates and disease
discordant probability of initially unaffected co-twins were examined using the proband
method and Kaplan-Meier method, respectively.
Results Genetic susceptibility to type 2 diabetes was suggested by higher concordance
rates in MZ compared to DZ twins at baseline and after follow-up (53% vs. 36%, and
77% vs. 53%); lower disease discordant probability after 15 years in MZ (22%) vs. DZ
twins (58%) (p<0.0001); a shorter time interval between the first and second diagnosis in
MZ (5.79+4.32 years) compared to DZ concordant pairs (7.39+4.17 years) (p= 0.037);
and a higher proportion of parents with diabetes among concordant pairs than discordant
pairs. However, among MZ twins, disease discordant probability was longer among those
46

who shared the same room for 16 years or longer compared to those who moved apart
sooner (28.8% vs. 7.6%) (p=0.0177).
Conclusions Our findings support prior estimates of type 2 diabetes concordance in MZ
twins but suggest that concordance in DZ twins may have been underestimated.
Furthermore, disease discordant probability may be modified by shared early
environmental factors which remain to be determined.
Keywords Type 2 diabetes; Monozygotic; MZ; Dizygotic; DZ; Disease discordant
probability

3.2 Introduction
Various environmental risk factors in adult life such as obesity, physical activity, and diet
have been associated with development of type 2 diabetes in the general population
[Wasserfall et al. 2006, Ahmann. 2006, Blonde et al. 2007], and genetic susceptibility has
been suggested based on evidence that a greater concordance for type 2 diabetes is found
in monozygotic (MZ) twin pairs (ranging from 14-79%) compared with dizygotic (DZ)
twin pairs (ranging from 16-35%) [Gottlieb et al. 1968, Tattersall et al. 1972, Barnett et al.
1981, Newman et al. 1987, Poulsen et al. 1999, Poulsen et al. 2009, Condon et al. 2008].
In addition, support for a genetic susceptibility has been provided by various studies
[Gottlieb et al. 1968, Tattersall et al. 1972, Barnett et al. 1981, Newman et al. 1987,
Poulsen et al. 1999, Poulsen et al. 2009, Condon et al. 2008, Serjeantson et al. 1984]
showing that having a family history of type 2 diabetes increases risk and that parents and
(non-twin) siblings of cases also have a 2-6 fold higher risk [Serjeantson et al. 1984].
47

Furthermore, more than 25 various susceptibility variants for type 2 diabetes and more
than 50 diabetes-related traits have been identified [Stancakova et al. 2009, Gloyn et al.
2008, Schmidt et al. 2008, Ridderstrale et al. 2009, Doria et al. 2008].
While previous twin studies in 1980’s have provided estimates of crude disease
concordance rates by zygosity, the estimates may have been confounded by recruitment
of twin pairs through diabetic clinics, where only the more severe cases of diabetes may
be seen [Gottlieb et al. 1968, tattersall et al. 1972, Barnett et al. 1981, Cockburn et al.
2001] and by disproportionate participation by concordant MZ as compared to
concordant DZ twin pairs in response to advertisements. The more recent studies have
provided susceptibility evidence using population-based twin registries [Poulsen et al.
1999, Poulsen et al. 2009, Condon et al. 2008] and the present study adds support to the
more recent population-based twin studies [Poulsen et al. 1999, Poulsen et al. 2009,
Condon et al. 2008] using data from twins ascertained through a population-based twin
registry, the California Twin Program (CTP), which provides less biased ascertainment of
twins, independent of disease status [Cockburn et al. 2001, Cockburn et al. 2002]. In
addition, there have been no published estimates of the interval between diabetes
diagnoses in concordant twin pairs or on the role of shared early environmental factors on
diabetes risk. Accordingly, we investigated the time to diagnosis of diabetes in co-twins
and the role that the shared early environment factors might play in affecting diabetes risk.

48

3.3 Materials, Methods, and Results
3.3.1 Source of Twin Pairs
The methods for the development of the California Twin Program (CTP) have been
previously described [Cockburn et al. 2001, Cockburn et al. 2002]. Briefly, this cohort of
native born California twins was initiated in 1989 by obtaining records for multiple live
births occurring between 1908 and 1982 from the California Department of Vital
Statistics, and the twin pairs were initially obtained by the birth records reported to the
California Department of Vital Statistics on whether or they were multiple live births.
The zygosity (classified as monozygotic, dizygotic, or unknown) of the twin pairs were
tentatively established by the perceived zygosity based on the answers to questions about
similarity [Kasriel et5 al. 1976] asked in the baseline questionnaire. After human
subject’s approval was obtained, located twins were mailed a 16 page baseline
questionnaire in 1992 and 1998-2001 based on their birth year (beginning with the older
twins). The questionnaire included questions on self-reported diseases including type 2
diabetes [Cockburn et al. 2002]. In addition, each twin was also asked about conditions
that their twins had and provided proxy information for some lifetime exposures. The
questionnaire was mailed to 136,156 twin individuals and 37.9% (51,609) responded
which was adjusted to 44% after a study of non-response [Cockburn et al. 2001]. A
higher response rate was found among females; although, fewer older females were
located due to name changes. In general, the twin individuals who returned the mailed
questionnaires did not differ from non-respondents in terms of age, sex and ethnicity, and
the distribution of twins by zygosity was as expected based on Weinberg’s rule
49

[Cockburn et al. 2001, Cockburn et al. 2002]. In comparison to the California native
born population, the responding twins were slightly less educated. In general the CTP
can be used as a representative sample of the twin population born in California.

3.3.2 Follow-up Study
Among the CTP twins who were mailed questionnaires in 1992 there were initially 393
twin pairs with at least one member reporting a previous diagnosis of type 2 diabetes.
They included 643 twin individuals (500 from 250 pairs with both members responding
and 143 single respondent pairs). After human subject’s approval was obtained, these
individuals were mailed a 3-page survey annually between January, 1997 and December,
2001. Specific information regarding the initial type 2 diabetes diagnosis (including the
date of diagnosis, the tests used to make the diagnosis, and the prescribed treatments),
occurrence and details of a diagnosis of type 2 diabetes in the initially unaffected co-twin,
and a family history of type 2 diabetes in the first-degree relatives was obtained. The
annual mailings of the survey continued until a twin pair became disease concordant by
reporting a second type 2 diabetes diagnosis in the initially unaffected twin and
information was provided to confirm both the first and second diagnoses. Otherwise,
pairs remaining disease discordant were sent annual mailings until the end of the study
follow-up date in December, 2001, or until death or loss to follow-up. It is important to
note that the actual follow-up period of disease discordant probability in twins could be
longer than 5 years (the follow-up date between January, 1997 and December, 2001) due
to the gap of 6 years from original CTP in 1991/1992 to the start of the follow up date in
50

1997 as well as the retrospective follow up for twins whose first self-reported date of type
2 diabetes diagnosis was prior to the start of original CTP study start date in 1991/1992.

3.3.3 Validation of Proxy Information
When responses from only one twin in pair were available, it was necessary to use the
respondent’s proxy information about the non-reporting co-twin to include that pair in the
analyses. Validity (sensitivity and specificity) of proxy type 2 diabetes information was
examined using 209 double respondent twin pairs for whom both self-reported and proxy
information was available. The sensitivity and specificity for proxy reports of type 2
diabetes (using the self-report as the gold standard) were 93.9% and 95.1%, respectively,
and no difference was seen by sex and zygosity. Therefore, the proxy information was
used to include the single respondent pairs in the analyses.

3.3.4 Verification of Self-reported Type 2 Diabetes
A set of inclusion/exclusion criteria was developed to indirectly verify self-reported type
2 diabetes diagnoses in consideration of at least one category of the self-reported
information such as age at diagnosis (>35 years), diagnostic methods (Yes/No on e.g.,
A1C, fasting glucose, and etc.), prescribed treatment (oral diabetes pill, lifestyle
modification of physical activities, diets), and duration of insulin use (<5 years of use).
Although the permission to review medical records was not sought, and no information
was available on diabetes-related diagnostic tests such as oral glucose tolerance test
values, circulating antibodies to islet cell antibodies, GAD antibodies or insulin, or other
51

laboratory test values to verify the clinical diagnosis of diabetes. The criteria using self-
reported age at diabetes diagnosis, self-reported diabetes diagnostic used, and self-
reported duration of insulin uses were consistent with standards used by earlier studies
when diabetes diagnostic test values or medical records were not available [Newman et al.
1987, Gottlieb et al. 1968, Medici et al. 1999, Kaprio et al. 1992, El Fakiri et al. 2007,
Huerta et al. 2009]. Based on the information recorded in the annual 3-page survey, the
twin had to have been diagnosed at 35 years and older, reported “Yes” on the diabetes
diagnostic tests, and used insulin for 5 years or less to be a verified case. We excluded
cases with diagnosis at age <35 years, who had used insulin for >5 years, or reported
having gestational diabetes.
Among those with known zygosity, there were a total of 395 self-reported diagnoses of
type 2 diabetes by the end of follow up. We excluded six because they did not meet the
age of diagnosis and insulin use criteria and eleven who self-reported as gestational
diabetes, resulting in 378 verified type 2 diabetes diagnoses (185 self-reported and 193
based on proxy information). The verification rate of self-reported type 2 diabetes
diagnosis using the inclusion/exclusion criteria is 95.7% (378/395). The 17 excluded
twins were included as controls, which resulted in a total of 290 pairs with valid type 2
diabetes diagnoses and zygosity included in the analyses.

52

3.3.5 Statistical Analysis
The proband concordance rate, which measures the risk of disease among co-twins of
affected twins were used. It is calculated using 2C/(2C+D), where C is the number of
disease-concordant pairs and D is the number of disease discordant pairs.
The time interval between the first twin’s diagnosis of type 2 diabetes and the diagnosis
of type 2 diabetes in the co-twin (or until the end of follow-up) was defined as the type 2
diabetes disease discordant probability. Dates of the initial diagnosis, second diagnosis in
the pair and/or end of the follow-up were required to calculate the interval. If either of
these dates was missing, they were estimated as follows: the date of questionnaire return
was used if the date of first diagnosis was missing and the mid-point of the follow-up
period was used when the date of the last follow-up survey was missing. A sensitivity
analysis showed no differences in results when using pairs with complete data compared
to those with estimated date, thus results are shown for the larger sample size including
the estimated dates.
The Kaplan-Meier method was used to calculate the length of the discordance time
interval within pairs and probability of a pair remaining disease discordant over time
since the first diagnosis in a pair called “disease discordant probability” [Kaplan et al.
1958]. The Mantel-Cox relative hazard model [Cox et al. 1984] was used to assess
differences in the length of the discordance time interval and the disease discordant
probability between subgroups of twins, stratified by zygosity, age at first diagnosis,
gender, and number of years sharing a room together when growing up.
53

This variable of “sharing room growing up in twin pairs” was based on categorized
responses (<6 years, 6-10 years, 11-14 years, 15-17 years, 18-21 years, 22-24 years, >25
years, and never) in the original questionnaire. The definition of “sharing room growing
up in twin pairs” was that the twin pairs lived and slept in the same bedroom when
growing up. The variable was analyzed as a dichotomous variable defined as sharing
room <16 years versus 16+ years due to the single digits counts in several categorized
responses (<6 years, 6-10 years, 11-14 years, and 18-21 years) and zero count in 22-24
years, >25 years, and never categorized responses The mid-point (16 years) of 15-17
years (responses of majority) was chosen to be the cut-off in the dichotomized variable
used in the later analysis.
Test for proportions (z-test) at significance level of 0.05 was used to compare the
percentage of pairs with a positive family history of type 2 diabetes diagnosis between
concordant and discordant pairs in both MZ and DZ twins.
Means and standard deviations (SD) were reported for age at diagnosis, age at
questionnaire return and years of follow-up.

3.3.6 Results
3.3.6.1 Response to Follow-up Survey
A total of 393 twin pairs (including 643 individuals) who were recruited in the CTP in
1992 were eligible and re-contacted with the annual 3-page follow up survey. 80% (514)
of the 643 study subjects (representing 305 pairs) responded to at least one of the
annually mailed surveys. The average age of the 305 respondent twin pairs (52.8+11.0
54

years) was similar to the 88 non-respondent twin pairs (52.5+10.9 years), at the beginning
of follow-up in 1997. There were also no differences by response status in the
distribution of twin pairs by zygosity, concordancy, race, or sex (data not shown). 15
respondent pairs had unknown zygosity as did 3 non-respondent pairs and were excluded
from further analyses.

3.3.6.2 Age and Concordance Rates
Due to lower recruitment of older females in the CTP, the average age of female-female
pairs at baseline was significantly younger than the male-male pairs for both zygosity
groups with the average age of the male-female pairs intermediate between the two
(Table 3). Overall, the average age for MZ pairs at baseline was slightly older (52.7
years) than for DZ pairs (50.2 years).
The MZ concordance rates were significantly higher than DZ concordance rates both at
baseline (53% vs. 36%; p=0.04) and at the end of follow up (77% vs. 53%; p=0.01)
There were no significant differences in the baseline concordance rates by sex despite the
differences in average age for either zygosity group. The percentage increase in
concordance between baseline and end of follow-up was 24% for MZ pairs and 17% for
DZ pairs.

55

Table 3: Age Distribution and Proband Concordance for Type 2 Diabetes at
Baseline (1997) and After the Follow-Up Study (2001) by Zygosity

At Baseline

After Follow-up
Concordance rate
% increase
MZ pairs
Age (Mean+SD)
Male-male pairs 58.9 +10.0 66.9 +10.0
Female-female pairs 45.4 +5.2 53.4 +5.2
Total pairs

52.7 +10.3 60.7 +10.2
Proband Concordance
a

Male-male pairs 0.57 (N=55) 0.79 (N=55)
c
22%
Both twins affected (C) 22 36
One twin affected (D) 33 19
Female-female pairs 0.46 (N=37) 0.72 (N=37)
c
26%
Both twins affected (C) 11 21
One twin affected (D) 26 16
Total pairs 0.53 (N=92) 0.77 (N=92
b
24%
Both twins affected (C) 33 57
One twin affected (D)

59 35
DZ Pairs
Age (Mean +SD)
Male-male pairs 54.3 +9.1 62.3 +9.2
Female-female pairs 45.5 +8.8 53.5 +9.1
Male-female pairs 52.5 +10.9 60.5 +11.2
Total pairs 50.2 +10.0 58.2 +11.0

Proband Concordance
a

Male-male pairs 0.35 (N=79) 0.55 (N=79)
c
20%
Both twins affected (C) 17 30
One twin affected (D) 62 49
Female-female pairs 0.36 (N=37) 0.57 (N=37)
c
21%
Both twins affected (C) 8 15
One twin affected (D) 29 22
Male-female pairs 0.38 (N=82) 0.50 (N=82)
c
12%
Both twins affected (C) 19 27
One twin affected (D) 63 55
Total pairs 0.36 (N=198) 0.53 (N=198)
b
17%
Both twins affected (C) 44 72
One twin affected (D)

154 126
a
(2C/2C+D)
b
P-value <0.05 for difference in proportions
c
P-value>0.05 for difference in proportions

56

3.3.6.3 Discordant Time Interval between 1
st
Diagnosed and 2
nd
Diagnosed Twin in
Concordant Pairs
Overall, based on pairs with complete and estimated data, the 92 MZ twin pairs had a
shorter mean discordance time interval (until the second twin was diagnosed or the end of
follow-up) compared to the 198 DZ twin pairs (7.0 +4.6 vs. 13.1 +8.7 years; p=0.001 for
difference of means). The mean disease discordant probability between diagnoses within
concordant pairs with complete data was also shorter for 36 MZ pairs than 51 DZ pairs
(5.79 +4.32 years for MZ pairs and 7.39 +4.17 for DZ pairs, p=0.037 for difference of
means).

3.3.6.4 Kaplan Meier Estimates of Discordance Time Survival
The probability of survival without type 2 diabetes developing in the co-twin declined
much more rapidly in MZ pairs than in DZ pairs based on 290 pairs (92 MZ and 198 DZ)
with complete or estimated data (Figure 2, p<0.0001). Beginning with 10 or more years
after diagnosis, the probabilities of pairs remaining disease discordant were significantly
different by zygosity (0.37 for MZ and 0.72 for DZ twins, p=0.03). At 15 or more years
after the first twins developed type 2 diabetes, the life-table estimated probabilities of
type 2 diabetes disease discordance for MZ and DZ twins were 0.22 and 0.58,
respectively (p<0.0001).

57

Figure 2: Probability of Twin Pairs Remaining Diabetes Discordant by Time
Since First Diagnosis in Pair By Zygosity (Monozygotic twins=MZ
vs. Dizygotic twins=DZ; p-value<0.0001)

0. 00
0. 25
0. 50
0. 75
1. 00
agedxdi f
0. 0 2. 5 5. 0 7. 5 10. 0 12. 5 15. 0 17. 5 20. 0
S T R A T A : t w i n= D Z C ensored t w i n= D Z t w i n= M Z C ensored t w i n= M Z

Within each zygosity group, disease discordant probability did not differ by sex or age at
diagnosis of the first twin (data not shown). However, for both MZ and DZ twin pairs,
disease discordant probability was lower in twins who shared a room for less than 16
years growing up compared to those sharing a room for 16 or more years, and the
differences were larger and statistically significant among MZ twins. After 15 years the
disease discordant probability for the MZ co-twins who shared a room with their diabetic
twins for less than 16 years was 7.6% compared to 28.8% for those staying together for
16 or more years (p=0.0177) (Figure 3).

58

Figure 3: Probability of MZ Twin Pairs Remaining Diabetes Discordant by
Years Sharing A Room Growing Up (<16 years=seproom 1 vs. 16+
years=seproom 2; p-value<0.0177)
0. 00
0. 25
0. 50
0. 75
1. 00
agedxdi f
0. 0 2. 5 5. 0 7. 5 10. 0 12. 5 15. 0 17. 5 20. 0
S T R A T A : seproom =1 C ensored seproom =1 seproom = 2 C ensored seproom = 2

3.3.6.5 Family Members with Type 2 Diabetes
For both 92 MZ (57 concordant vs. 35 discordant) and 198 DZ (72 concordant vs. 126
discordant) pairs, there were significantly higher and similar proportions with affected
family members among the concordant compared to the discordant pairs (Table 4).
Nearly 80% of concordant pairs (79% and 78% for MZ and DZ pairs, respectively) had at
least one family member affected (including a parent, grandparent, or non-twin sibling),
while 54% and 63% of MZ and DZ discordant pairs, respectively, reported a family
member affected with type 2 diabetes. These differences between concordant and
discordant pairs were statistical significant for both MZ and DZ twins (p<0.05, z-test).

59

Table 4: Percent with Family Members Affected by Type 2 Diabetes by
Family Relationship and Type 2 Diabetes

Family Relationship of Affected
Relative
MZ twins N=92 pairs DZ twins N=198 pairs
Concordant Discordant Concordant Discordant
All pairs 57 (100%) 35 (100%) 72 (100%) 126 (100%)
Parents (Mom or Dad)
No 16(28%) 27(78%) 27(37%) 68(54%)
Yes 41(72%)
a
8(22%)
a
45(66%)
a
58(46%)
a

Grandparents (Paternal or
Maternal)

No 31(55%) 30(86%) 35(49%) 93(74%)
Yes 26(45%)
a
5(14%)
a
37(51%)
a
33(26%)
a

Siblings (other than their co-
twins)

No 31(54%) 28(80%) 45(62%) 108(86%)
Yes 26(46%)
a
7(20%)
a
27(37%)
a
18(14%)
a

Any first-degree family member
(parents or siblings)

No 14(25%) 26(74%) 21(29%) 58(46%)
Yes 43(75%)
a
9(26%)
a
51(71%)
a
68(54%)
a

Any family member
(parents or grandparents or
siblings)

No 12(21%) 16(46%) 16(22%) 47(37%)
Yes 45(79%)
a
19(54%)
a
56(78%)
a
79(63%)
a

a
comparisons between concordant and discordant pairs P<0.05 using test of proportions (z-test)
b
comparisons between concordant and discordant pairs P>0.05 using test of proportions (z-test)

3.4 Discussion
Our findings that type 2 diabetes concordance rates differ by zygosity add support to
previous evidence suggesting that genetic susceptibility affects the development of type 2
diabetes [Poulsen et al. 1999, poulsen et al. 2009, Condon et al. 2008, Gottlieb et al. 1968,
Medici et al. 1999]. Our MZ proband concordance rate at the end of follow-up (when the
average age of the twin pairs was 58-60 years) of 77% is similar to a study of older (i.e.
average age of 55-60 years) volunteer twins [Gottlieb et al. 1968] and to another 1997
60

UK study [Kasriel et al. 1976] of 44 MZ similarly aged twin pairs. However our DZ
proband concordance rate of 53% was higher than found in previous volunteer studies,
which found a DZ concordance rate between 24-35% [Newman et al. 1987, Kaprio et al.
1992]. The volunteer ascertainment method in earlier studies of 1980’s [Gottlieb et al.
1968, Tattersall et al. 1972, Barnett et al. 1971, Newman et al. 1987] may be expected to
over-ascertain MZ concordant pairs compared to discordant pairs since the occurrence of
disease in both members of identical pairs may be considered more notable to family
members and friends and result in increased awareness and response. However this may
not occur for DZ pairs, therefore the higher concordance rate among DZ pairs reported
here may indicate that the actual rate in a population-based setting is higher than
previously reported.
Other support for genetic susceptibility to type 2 diabetes was demonstrated by our
finding of a higher proportion of concordant pairs with affected family members
compared to discordant pairs regardless of zygosity. Specifically, the proportion of
siblings with type 2 diabetes for MZ twins in concordant and discordant pairs (46% vs.
20%) which we found is consistent with a previous study [Newman et al. 1987] but we
are the first to also find a similar difference based on concordant status in DZ twins.
In addition to these expansions on previous results, our study adds further support to
previous findings and a novel finding on shared early environment. First, the
significantly shorter discordant time interval in MZ compared to DZ twins that were not
affected by sex and age at diagnosis of diabetes of the first twin strengthens the
likelihood of a genetic susceptibility. Second, we found that that sharing a room for a
61

longer time in childhood significantly improved the probability of remaining disease
discordant among MZ twins; there was a trend toward similar improvement in DZ twins
that did not reach statistical significance as well. A possible explanation for this finding
is that the longer shared environment may promote a healthier lifestyle in the unaffected
twin due to their greater awareness of the disease that could delay or forestall
development of diabetes in genetically susceptible individuals. In the other hand, it is
also possible that twin pairs from a lower social-economic status may move apart earlier
upon/after teenage, and furthermore have an unhealthier lifestyle, which may contribute
to the development of type 2 diabetes. Therefore, future studies to study this relationship
will be needed. Other study in psychiatry has provided evidence of possible mechanisms
by which genetic susceptibility may be modified [Kendler et al. 2009]. One gene that has
been associated with development of type 2 diabetes is the Pro12Ala genetic variant
which regulates adipocyte differentiations affecting the cellular insulin sensitivity
[Riddrtstrale et al. 2009, Doria et al. 2008, Deeb et al. 1998]. Potential interactions
between Pro12Ala variants and different risk factors for type 2 diabetes including BMI
(body mass index) and insulin sensitivity have been shown by previous studies [Gloyn et
al. 2008, Schimdt et al. 2008, Doria et al. 2008, Medici et al. 1999]. The PPARγ gene
and other genetic factors, such as TNF-α, are also thought to affect the mechanism of
adipose tissue differentiation at the cellular level and subsequently contribute to
appearance of obesity [Deeb et al. 1998, Hotamisligil et al. 1996]. Form the reviews
[Doria et al. 2008, Ridderstrale et al. 2009] of currently available Genome-Wide
Association (GWA) Studies (, several other obesity genes including FTO, leptin, MC4R
62

(melanocortin 4 receptor), INSIG2 (insulin-induced gene 2), etc have been identified to
be association with the development of type 2 diabetes. In addition, the thrifty genotype
and thrifty phenotype hypotheses have also been combined into the concept of adverse
effect of under-nutrition [Bloomgarden et al. 2004] which the effects of intrauterine
under-nutrition and subsequent over-nutrition lead to insulin resistance, while the thrifty
genotype hypothesizes a survival advantage accrued from many generations of episodic
malnutrition increasing the expression of this state. A U-shaped relationship has been
suggested that those persons with highest prepubertal body weight and those with lowest
birth weight are particularly at risk of insulin resistance and diabetes.
Limitations of our findings should be noted. Since the baseline survey was a cross-
sectional study; the information on diabetes risk factors (e.g. obesity, diet, exercise) prior
to the diagnosis was not available. Also the diagnosis of type 2 diabetes was based on
self-report; laboratory confirmation of diabetes, insulin resistance, and lack of
autoimmunity was not available. While, our criteria for validation of the diagnosis (i.e.
age of self-reported diagnosis by a physician and years of insulin use) have been used in
other studies [Tattersall et al. 1972, Barnett et al. 1981, Newman et al. 1987, Gottlieb et
al. 1968], a more specific validation study to compare these criteria to laboratory-based
diagnostic tests has not been conducted. Thus, misclassification of the diagnosis of type 2
diabetes may have occurred. However, it would be unlikely that differential
misclassification in self-reports by zygosity would have occurred, thus our findings
regarding differences in concordance rates by zygosity and relative differences of time
intervals between diagnoses would be internally valid. The other studies based on twins
63

that we have used for comparison have also used self reported data [Newman et al. 1987,
Gottlieb et al. 1968, Medici et al. 1999, Kaprio et al. 1992, El Fakiri et al. 2007, Huerta et
al. 2009]. In addition, we were not able to assess the actual number of years twins shared
room together due to the pre-defined categorical responses in the original questionnaire.
Therefore, this variable was not able to be analyzed as a continuous variable (obtaining
estimates of means and standard deviations (SD)) and we could not assess if there was a
dose-response effect related to the number of years a room was shared.
Despite these limitations, the study has provided concordance data from a much larger
number of affected pairs drawn from a more representative sample of twins than has been
reported on before, and the follow-up study has provided the ability to assess the
discordance time interval between MZ and DZ twins. While our concordance rates for
MZ twins were similar to those reported previously, we found higher concordance rates
in DZ twins when compared to other studies based on both volunteer and population-
based ascertainment methods and this implies that DZ concordance rates may have been
underestimated in those previous studies. Our finding that a longer time of sharing a
room among MZ twins was associated with a delayed diagnosis of diabetes in the second
twin suggests that environmental factors may modify the risk of developing diabetes
among genetically susceptible individuals. This finding provides incentive to understand
mechanisms that may be involved. Since a blood sample was not obtained in this study,
we could not determine specific genetic information such allele frequencies for
polymorphisms that may be related to this finding and we did not have the ability to study
potential gene-environmental modification. Therefore, further studies aimed at looking at
64

specific genetic variants and their interactions with various environmental factors both in
adult and teenage life are needed.

65

Chapter 4: Paper Two

The Association of Smoking and Height in Teenage Years with Type 2
Diabetes Risk in California Twins

4.1 Abstract
Background Type 2 diabetes is thought to be a heterogeneous and complex disease with
a multi-factorial etiology consisting of genetic and environmental factors and often
manifests in adult life. We aimed to assess the role of environmental factors using a
population-based twin registry to control totally or partially for genetic factors.
Methods Using 161 disease discordant twin pairs (126 dizygotic (DZ) and 35
monozygotic (MZ) pairs), a matched case-control study nested in the California Twin
Program (CTP) was conducted. Conditional multivariable logistic regression analysis was
used to estimate odds ratios for childhood/adolescent and adult lifestyle factors and risk
of type 2 diabetes.
Results In 95 DZ disease discordant like-sex twins, a faster growth rate (adjusted odds
ratio (OR)=2.37, 95% confidence interval (CI): 1.00-7.03) and being the taller twin in the
pair (adjusted OR=3.32, 95%CI: 1.27-6.78) in teenage years were independently
associated with increased risk of developing type 2 diabetes. In addition, adolescent
smoking was also associated with an increased risk for type 2 diabetes in 95 DZ disease
66

discordant like-sex twins (adjusted OR=1.83, 95%CI: 1.00-3.49). Similar patterns were
also observed in MZ disease discordant twin pairs, but were not statistically significant.
Conclusions Even among genetically similar individuals, differences in
childhood/adolescent growth factors were associated with the development of type 2
diabetes, providing additional evidence that increased body mass and faster development
in childhood can increase risk of type 2 diabetes later in life.
Keywords Type 2 diabetes; Genetic susceptibility; Body mass Index, Smoking,
Monozygotic; MZ; Dizygotic; DZ; Adjusted Odds Ratio; Adjusted OR

4.2 Introduction
While Type 1 (insulin-dependent) diabetes mellitus, which occurs primarily in young
children and adolescents, is known to have a strong genetic susceptibility, type 2 diabetes,
formerly known as non-insulin-dependent diabetes, is thought to be a heterogeneous and
complex disease with a multi-factorial etiology consisting of both genetic and
environmental factors and often manifests in adult life [Beck-Nielsen et al. 1994]. A
genetic risk has been suggested from evidence that a greater concordance for type 2
diabetes is found in monozygotic (MZ) twin pairs, ranging from 14% to 79%, compared
with 16-35% among dizygotic (DZ) twin pairs in population-based twin studies including
our previous report based on the twins included in the current study [Barnett et al. 1981,
Newman et al. 1987, Hawkes et al. 1997, Tsai et al. submitted 2011, Gottlieb et al. 1968,
Tattersall et al. 1972, Poulsen et al. 1999, Poulsen et al. 2009, Condon et al. 2008].
67

In addition, various environmental factors have also been suggested. We previously
reported that the genetic susceptibility may be modified by the length of time the twins
shared a room with each other, with lower concordance and greater time between a first
and second diagnosis within MZ twin pairs if they shared a room together in childhood
for 16+ years vs. less than 16 years [Tsai et al. submitted 2011]. Other studies have
shown that obesity, exercise, and diet are related to risk of Type 2 diabetes. Higher BMI
(body mass index), especially if associated with abdominal/visceral obesity, has been
shown to be associated with increased risk [Chan et al. 1994, Boyko et al. 2000, Despres
et al. 1991, Ohlson et al. 1985, Wiegand et al. 2004]. Higher intake of dietary fat, high
intake of refined carbohydrates, and low amount of fiber has also been shown to increase
risk of type 2 diabetes development [Marshall et al. 1991, Salmeron et al. 1997a,
Salmeron et al. 1997b]. Being sedentary was shown to contribute to a higher risk of
developing type 2 diabetes [Feskens et al. 1995, Liu et al. 2000, Kriska et al. 1993]
whereas regular weekly exercise was shown to be related to a reduced risk [Manson et al.
1991].
Since genetic factors may also be related to some of these risk factors, obesity in
particular [Chan et al. 1994, Boyko et al. 2000, Despres et al. 1991, Ohlson et al. 1985,
Wirgand et al. 2004], it is difficult to determine the role that lifestyle may play
independently of genetic susceptibility. Here, we have examined further the role of
environmental influences in the development of type 2 diabetes using matched case-
control twin pairs to control totally (MZ twins) or partially (DZ twins) for genetic factors.

68

4.3 Materials, Methods, and Results
4.3.1 Source of Twin Pairs
The subjects in this study were twins who were originally enrolled in the California Twin
Program (CTP). The methods for the development of the California Twin Program
(CTP) have been previously described [Cockburn et al. 2001, Cockburn et al. 2002].
Briefly, this cohort of native born California twins was initiated in 1989 by obtaining
records for multiple live births occurring between 1908 and 1982 from the California
Department of Vital Statistics. After human subject’s approval was obtained, 136,156
located twins were mailed a 16 page baseline questionnaire in 1992 and 1998-2001 based
on their birth year (beginning with the older twins). The questionnaire included questions
on self-reported diseases including type 2 diabetes as well as on zygosity [Cockburn et al.
2001]. In addition, each twin was also asked about conditions that their twin had and
provided proxy information for some lifetime exposures. 37.9% (51,609) responded
which was adjusted to 44% after a study of non-response [Cockburn et al. 2002]. A
higher response rate was found among females; although, fewer older females were
located due to name changes. In general, the twin individuals who returned the mailed
questionnaires did not differ from non-respondents in terms of age, sex and ethnicity, and
the distribution of twins by zygosity was as expected based on Weinberg’s rule
[Cockburn et al. 2001, Cocokburn et al. 2002]. In comparison to the California native
born population, the responding twins were slightly less educated. In general the CTP
can be used as a representative sample of the twin population born in California.

69

4.3.2 Follow-up Study
Among the CTP twins who were mailed questionnaires in 1992 there were initially 393
twin pairs with at least one member reporting a previous diagnosis of type 2 diabetes.
They included 643 twin individuals (500 from 250 pairs with both members responding
and 143 single respondent pairs). After human subject approval was obtained, these
individuals were mailed a 3-page survey annually between January, 1997 and December,
2001. Specific information regarding the initial type 2 diabetes diagnosis (including the
date of diagnosis, the tests used to make the diagnosis, and the prescribed treatments),
occurrence and details of a diagnosis of type 2 diabetes in the initially unaffected co-twin,
and a family history of type 2 diabetes in the first-degree relatives was obtained. The
annual mailings of the survey continued until a twin pair became disease concordant by
reporting a second type 2 diabetes diagnosis in the initially unaffected twin and
information was provided to confirm both the first and second diagnoses. Otherwise,
pairs remaining disease discordant were sent annual mailings until the end of the follow-
up study on December 31, 2001, or until death or loss to follow-up. It is important to
note that the actual follow-up period of discordance time interval in twins could be longer
than 5 years (the follow-up date between January, 1997 and December, 2001) due to the
gap of 6 years from original CTP in 1991/1992 to the start of the follow up date in 1997
as well as the retrospective follow up for twins who were first self-report type 2 diabetes
diagnosis prior to the start of original CTP study start date in 1991/1992.

70

4.3.3 Validation of Proxy Information
When responses from only one twin in pair were available, it was necessary to use the
respondent’s proxy information about the non-reporting co-twin to include that pair in the
analyses. Validity (sensitivity and specificity) of proxy type 2 diabetes information was
examined using 209 double respondent twin pairs for whom both self-reported and proxy
information was available. The sensitivity and specificity for proxy reports of type 2
diabetes (using the self-report as the gold standard) were 93.9% and 95.1%, respectively,
and no difference was seen by sex and zygosity. Therefore, the proxy information was
used to include the single respondent pairs in the analyses.

4.3.4 Verification of Self-reported Type 2 Diabetes
The self-reported type 2 diabetes diagnoses were validated according to previously
developed inclusion/exclusion criteria [Tsai et al. submitted 2011] which required that the
subject was aged 35 or older at the time of diabetes diagnosis, had not used insulin for
more than 5 years, and did not report having gestational diabetes. Although the
permission to review medical records was not sought, and no information was available
on diabetes-related diagnostic tests such as oral glucose tolerance test values, circulating
antibodies to islet cell antibodies, GAD antibodies or insulin, or other laboratory test
values to verify the clinical diagnosis of diabetes, the criteria using self-reported age at
diabetes diagnosis and self-reported duration of insulin use were consistent with
standards used by earlier studies when diabetes diagnostic test values or medical records
71

were not available [Newman et al. 1987, Gottlieb et al. 1968, Medici et al. 1999, Kaprio
et al. 1992, El Fakiri et al. 2007, Huerta et al. 2009].

4.3.5 Baseline Covariates
The following adult related obesity, dietary and exercise related variables, that were
obtained at the time the baseline questionnaire was completed, were studied: body mass
index (BMI) (kg/m
2
), number of alcohol drinks consumed per week, number of times
eating diary products per week, number of times of high-fat food consumption per week,
number of hours of breathe-easy exercise per week, and number of hours of breathe-hard
exercise per week.
The recall period for alcohol consumption was 14 days prior to the time the baseline
questionnaire being completed while the physical exercise questions referred to the past
year. The ‘breathe-hard’ exercises included jogging, swimming, aerobics, bicycling,
tennis, and other activities, while activities such as walking, playing golf, gardening, and
other activities were defined as ‘breathe-easy’ exercises. Dietary information was based
on how often (number of times on average) respondents consumed beef, cottage cheese,
cheese, eggs, beans, fish, bread, carrots, broccoli/spinach/greens, milk, and citrus on a
weekly basis at the time the baseline questionnaire was completed. High-fat foods were
defined as beef and eggs; dairy foods included of cheese, cottage cheese, yogurt, and
milk. Adolescent growth rate was based on the response to ‘which twin grew faster at
puberty’, and height was based on which twin was taller (>1 inches) in the teenage years.
The adult BMI (kg/m
2
) variable was calculated based on the weight and height at the time
72

the baseline questionnaire was completed (in 1992). Smoking was defined as never
smoked, started smoking before age 19, and started smoking after age 19.
The exposures which were measured at the time of the baseline questionnaire or within
the year prior to its completion may reflect lifestyle factors after the time of onset of the
Type 2 diabetes diagnosis for those pairs in which the case-twin reported type 2 diabetes
occurring prior to the time the baseline questionnaire was completed. Thus cause and
effect may not be able to be determined for some exposures. However the exposures
occurring in adolescence (i.e. growth during puberty and smoking before 19) would be
more likely to have occurred prior to disease onset.

4.3.6 Statistical Analysis
Statistical analyses were performed with SAS 9.1 for Windows (2000, SAS Institute Inc.,
Cary, NC, USA). Conditional logistic regression, using the SAS Proc Phreg program,
was used to calculate the multivariate-adjusted matched odd ratios for the adult lifestyle
and childhood/adolescent factors in relation to the development of type 2 diabetes. The
twin pairs included in this study were those who were identified as a disease discordant
pair at the time of the baseline survey (in 1992), the case’s diagnosis was confirmed by
information received in the follow-up study, and the co-twin remained unaffected though
the end of the follow-up study (in 2001). These analyses were based on 161 disease
discordant twin pairs (35 MZ pairs and 126 DZ pairs) with validated diagnoses.
Statistical significance was defined at P-value < 0.05.

73

4.3.7 Results
At the time of completion of the baseline questionnaire, the average ages of 35 MZ and
126 DZ diabetes discordant pairs were 50.9 +12.1 and 48.4 +10.3 years, respectively
(Table 1). 16 pairs of the 35 MZ discordant pairs were male-male pairs and the other 19
pairs were female-female pairs (Table 5). Among the 126 DZ discordant pairs, 45 pairs
were male-male pairs, 50 pairs were female-female pairs, and 31 pairs were male-female
pairs (Table 5).
In the following conditional logistic regression analyses for both adult and adolescent
exposures, all ORs have been adjusted for race, education, BMI, and physical activity,
and dietary consumption, unless otherwise noted. Since weight, growth rate, and height
are affected by gender, all of these analyses have been restricted to like-sex pairs.

Table 5: Number of Type 2 Diabetes Discordant Pairs by Gender and Mean
Age at Baseline (1992) by Zygosity
Zygosity
MZ DZ

Male-male pairs 16 45
Female-female pairs 19 50
Like-sex pairs
a
35 95
Male-Female pairs - 31
Total pairs 35 126

Age at baseline (Mean +SD)
Male-male pairs 52.7 +11.4 49.2 +9.7
Female-female pairs 47.4 +8.2 46.7 +9.8
Like-sex pairs
a
49.8+9.7 47.9+9.8
Male-female pairs - 48.8 +11.1
Total pairs 50.9 +12.1 48.4 +10.3

a
Like-sex pairs included female-female pairs and male-male pairs

74

4.3.7.1 Adult Lifestyle Factors
Among the 95 DZ discordant like-sex pairs, adult BMIs of 26-30 and >30 (measured at
the time of the baseline questionnaire), where 26-30 is considered overweight and >30 is
considered obese, were associated with increased adjusted odd ratios of 1.51 and 2.27,
respectively (p=0.04 for trend) for disease when compared to having a BMI of less than
26 (Table 6). Similar results were found for like-sex male and female pairs when
analyzed separately (data not shown). In the adjusted conditional logistic regression
model, greater exercise (including a combination of both low and high intensity activity)
was associated with a reduced risk of Type 2 diabetes and the effect was stronger as the
number of exercise hours per week increased (p=0.05 for trend) (Table 6). Consuming
diary products more than once a week was associated with an increased risk of type 2
diabetes in DZ twins (adjusted OR=1.11; 95%CI: 1.01-2.09) compared to less frequent
consumption (Table 6). More than once a week consumption of high-fat food such as
beef and eggs also was associated with a higher OR (adjusted OR=1.19; 95% CI: 1.00-
2.79) compared to consuming high-fat food less than weekly (Table 6). When further
adjusting for total calorie index, similar patterns were found for both dairy and high-fat
foods; however, the results were not statistically significant (Table 6). Moderate drinking
(1.5 times per week) reduced the association with type 2 diabetes by 43% (Table 6)
compared to drinking occasionally (less than 1.5 times per week). The results for the
MZ twins showed a similar pattern (although non-significant) as for the DZ twins (data
not shown).

75

Table 6: Adjusted Odds Ratios for Selected Adult Lifestyle factors in
Relation to the Development of Type 2 Diabetes among 95 DZ
Discordant Like-sex Twin Pairs
Exposure/twin pair by response Ratio of exposure,
discordant pairs
c

Adjusted odds
ratio †
d

95% CI
e
P-value
f

BMI at onset of type 2 diabetes
diagnosis

<26 23/20 Ref. -
26-30 21/11 1.51 1.00-4.02
>=30 15/5 2.27 1.09-7.07 0.04
No. of alcohol drinks per week
<1.5 31/33 Ref. -
1.5+ 10/21 0.57 0.19-0.98 0.05
No. of times eating dairy products
per week

0 33/31 Ref. -
1+ 18/13 1.11
1.34*
1.01-2.09
0.51-4.03*
0.02
0.49
No. of times eating high-fat food
per week

0 26/25 Ref. -
1+ 19/15 1.19
1.10*
1.00-2.79
0.36-3.21*
0.04
0.09
Hours of exercised weekly –
breath easy

<1.5 hr 27/25 Ref. -
1.5-2.5 hr 6/14 0.57 0.09-0.94
2.5+ hr 7/16 0.51 0.17-0.97 0.04
Hours of exercised weekly –
breath hard

<1.5 hr 14/16 Ref. -
1.5-2.5 hr 20/26 0.87 0.32-2.41
2.5+ hr 8/11 0.73 0.17-3.08 0.47
Hours of exercised weekly –
breath easy and breath hard

<2.5 hr 16/19 Ref. -
2.5-5.0 hr 6/10 0.62 0.18-1.47
5.0+ hr 8/14 0.51 0.11-0.99 0.05
†
All variables have been adjusted for age, gender, race, education, BMI, physical activity, alcohol
consumption, and dietary consumption of diary products and fat.
* Also adjusted for Total Calorie Index.
a
Include cheese, cottage, yogurt, and milk.
b
Include beef and eggs.
c
Total number of twin pairs in which case-twin was exposed and the unaffected cotwin was
unexposed/total number of twinpairs in which unaffected cotwin was exposed and the
case-twin was unexposed.

d
Adjusted odds ratio estimated using conditional logistic regression.

e
Confidence interval estimated using conditional logistic regression.

f
P-value estimated for trend.

76

4.3.7.2 Childhood/Adolescent Factors
Among 95 DZ discordant like-sex twin pairs, when compared to non-smokers, the
adjusted OR for developing type 2 diabetes was 1.83 (95% CI: 1.00-3.49) for teen
smokers (who started smoking at age of 19 or younger) and was 1.71 (95%CI: 0.68-4.17)
for adult smokers (initiated smoking initiation at age 19 or older (Table 3). Among DZ
discordant like-sex twin pairs, when compared to the twins who grew at the same or
slower rate as their co-twin during their puberty, an increased risk of 2.37 (95%CI: 1.00-
7.03) for the development of type 2 diabetes was observed in twins who grew faster than
their co-twins (Table 7). We then further compared twins by height in their teenage years
and found that those who were taller by >1 inches than their co-twin had an increased risk
of 3.32 (95%CI: 1.26-6.78) for the development of type 2 diabetes compared to twins
who had similar or shorter heights as their co-twins (Table 7).
A similar pattern was also observed in 35 MZ disease discordant twins although the
results were not statistically significant (Table 3). Faster growth at puberty was
associated with an elevated risk of 1.20 (95%CI: 0.62-13.37) for the development of type
2 diabetes later in life compared to the MZ twin pairs who grew at the same or slower
rate during puberty (Table 7). Likewise being taller >1 inches in the teenage years was
associated with an higher adjusted risk of 1.50 (95%CI: 0.69-11.49) for the development
of type 2 diabetes later in life compared to the MZ twin pairs who were as in the same or
shorter height (<1 inches difference) in their teenage years (Table 7).

77

Table 7: Adjusted Odds Ratio of Adolescent Smoking, and Puberty Growth
in Relation to the Development of Type 2 Diabetes by Zygosity
Exposure/twin pair by
response
Ratio of exposure,
discordant pairs
a

Adjusted odds
ratio †
b

95% CI
c
P-value
d

DZ like-sex
h
pairs (95 pairs)
Adolescent smoking
e

Never smoker 27/22 Ref. -
Adult smoker † 18/7 1.71 0.68-4.17
Teen smoker ‡ 15/6 1.83 1.00-3.49 0.05
Faster growth at puberty
f

No 26/31 Ref. -
Yes 28/10 2.37 1.00-7.03 0.05
Being taller >1 inches at teen
g

No 22/37 Ref. -
Yes 26/10 3.32 1.26-6.78 0.04

MZ Pairs (35 pairs)
Adolescent smoking
e

Never smoker 5/7
Adult smoker † 7/6 1.21 0.25-7.13
Teen smoker ‡ 6/4 1.48 0.34-7.93 0.48
Faster growth at puberty
f

No 14/11
Yes 7/3 1.20 0.62-13.37 0.27
Being taller >1 inches at teen
g

No 12/10
Yes 9/4 1.50 0.69-11.49 0.21
† Started smoking > 19 years old
‡ Started smoking <= 19 years old
a
Total number of twin pairs in which case-twin was exposed and the unaffected cotwin was
unexposed/total number of twinpairs in which unaffected cotwin was exposed and the case-twin
was unexposed.
b
Odds ratio estimated using conditional logistic regression.
c
Confidence interval estimated using conditional logistic regression adjusted for gender, race,
education, physical activity, BMI, alcohol consumption, and dietary consumption of diary products and fat.
d
P-value estimated for trend.
e
Never smokers as reference group.
f
Same and slower growth at puberty as reference group.
g
Same and shorter height (<1 inches difference) at teen as reference group.
h
Like-sex pairs included female-female pairs and male-male pairs

4.4 Discussion
The most interesting findings in this study are that earlier growth differences within twin
pairs and earlier smoking initiation prior to 19 were independently associated with
increased risk for the development of type 2 diabetes later in adult life. Similar to
previous evidence showing that an increasing rate of type 2 diabetes has been
78

accompanied by an increasing prevalence of adolescent obesity [Chan et al. 1994, Boyko
et al. 2000, Depres et al. 1991, Phlson et al. 1985, Wiegand et al. 2004], these findings
suggest that exposures during adolescence are important precursors to the development of
type 2 diabetes in adulthood. By utilizing disease discordant twin pairs in this study, we
have shown that these factors are important, even among individuals who share 50-100%
of their genome, thus suggesting that earlier growth and obesity may be related to type 2
diabetes independently of genetic susceptibility.
Evidence from genetic studies [Fajas et al. 1997, Fajas et al. 1998, Kubota et al. 1999,
Zabena et al. 2009, Permutt et al. 2005] of type 2 diabetes have suggested possible gene-
environment interaction, especially with obesity related genes, as an explanation of how
environmental factors may reduce and/or increase genetic susceptibility in developing
type 2 diabetes. Faster growth rate and being taller in teenage years may indirectly
indicate a teenage dietary habit of higher food consumption and higher overall calorie
intake which may continue into adulthood. It is known that a persistent habit of total
calorie over-intake over long period of time can contribute to obesity which may lead to
insulin insensitivity at the cellular level and subsequently lead to the development of type
2 diabetes [Chan et al. 1994, Boyko et al. 2000, Depres et al. 1991, Ohlson et al. 1985,
Wiegand et al. 2004]. Several obesity-related genes including FTO, ABCC8, CAPN10,
GCGR, GCK, KCNJ11, HNF4A, and SLC2A1, PPAR-gamma (Peroxisome
Proliferators-Activated Receptor gamma), and other genes, are thought to affect the
degree of obesity and the insulin resistance which could also lead to the subsequent
development of type 2 diabetes [Fajas et al. 1997, Fajas et al. 1998, Kubota et al. 1999,
79

Zabena et al. 2009, Permutt et al. 2005]. The effect of a combination of both a genetic
susceptibility and increased dietary intake requires further investigation.
Our finding of an increased risk with earlier age at smoking initiation is indirectly
supported by previous studies of adult smokers [Manson et al. 2000, Rimm et al. 1995].
Those studies have shown that [Manson et al. 2000, Rimm et al. 1995] long-term
adulthood cigarette smoking is associated with a modest increased risk in the
development of type 2 diabetes; however, there is no hypothesized biological mechanism
of how smoking may be associated with the development of diabetes.
In addition, our finding that moderate alcohol consumption was associated with a reduced
risk in the development of type 2 diabetes is consistent with previous studies [Joosten et
al. 2010, Boggs et al. 2010]. In a large cohort of 35,625 Dutch adults [Joosten et al.
2010], moderate alcohol consumption was associated with a reduced hazard ratio of 0.35
(95%CI: 0.17-0.72) and a study of 46,906 African American women [Boggs et al. 2010],
found that moderate alcohol consumption on a weekly basis was associated with a
reduced risk of 0.68 (95%CI: 0.57-0.81). Overall, our findings regarding BMI [Chan et al.
1994, Boyko et al. 2000, Depres et al. 1991, Ohlson et al. 1985, Wiegand et al. 2004],
physical activity [Feskens et al. 1995, Liu et al. 2000, Kriska et al. 1993], and diet [Hu et
al. 1999, Manson et al. 2000, Rimm et al. 1995] in the development of type 2 diabetes
were consistent with previous studies of similar factors among non-twin subjects.
Although our findings are of importance, there are several limitations that should be
noted. First, the exposure data was retrospectively collected from a cross sectional, self-
reported survey. Thus we cannot determine cause and effect, especially for adult related
80

exposure information. However, the similar direction and magnitude of our findings for
the adult lifestyle factors with previous studies [Feskens et al. 1995, Liu et al. 2000,
Kriska et al. 1993, Hu et al. 1999, Manson et al. 2000, Rimm et al. 1995], indicate that
the baseline survey results may likely reflect long-term lifestyle habits prior to the onset
of diabetes diagnosis in the diseased twins. A longitudinal twin study of substance use
has shown that a pattern of nicotine and alcohol use starting at early ages persists into
adult life [Kendler et al. 2009]. Furthermore, if there was bias, it may be likely that the
OR’s are underestimated because it would be more likely that a person with diabetes
would be reducing their weight and food consumption in order to control their disease
rather than increasing them. Nevertheless, future studies of twins should aim to obtain
prospective follow up information on lifestyle behaviors before, at onset, and after the
diabetes diagnosis. Second, the diagnosis of diabetes was self-reported and not
confirmed by medical records. However, the type 2 diabetes verification criteria using
self-reported age at diabetes diagnosis and self-reported duration of insulin uses was
consistent with standards used by earlier studies when diabetes diagnostic test values or
medical records were not available [Newman et al. 1987, Gautier et al. 2001, Dabelea et
al. 1998, Stern et al. 2000, Manson et al. 2000, Rimm et al. 1995]. Finally, we did not
have DNA on these twins to determine possible gene-environment interaction.
In conclusion, our results support previous findings that early weight, growth, and
smoking, as well as adult lifestyle factors related to diet, alcohol, and exercise contribute
to risk of (or are associated with) developing type 2 diabetes, even among individuals
totally or partially genetically matched. This adds to existing evidence that preventive
81

lifestyle habits beginning in childhood or adolescence are important to avoid developing
this potentially debilitating disease.

82

Chapter 5: Conclusion

Based on the review in chapter 1 and the study results of type 2 diabetes in California
Twin Program (CTP) described in chapter 2 and chapter 3, various specific genetic
variants and candidate genes have been found/suggested to associate with the
development of type 2 diabetes, and the CTP study results also provide consistent
evidence on the genetic susceptibility and novel findings on the potential influence in
shared environments and behavioral factors in childhood/adolescence. The genetic
susceptibility is further supported by the findings that diabetes concordance rates
increased over the follow up period and the concordance rates are higher in MZ twin than
in DZ twins before/after the follow up period, and this study also provides evidence on
novel findings that the longer the period twins shared room in teenage years, the longer
the disease-discordant probability, and the faster/slower growth rate in teenage years and
being a teen smoker also associates with an increased risk in developing type 2 diabetes.
Although the findings in this current study is noteworthy, there are limitations in the
study such as retrospectively collecting on self-reported information on history of disease
diagnoses and lifestyle factors; lack of lab test values for diabetes diagnosis; as well as
lack of biological specimens for genetic studies. In summary and conclusion, prospective
follow up study of healthy twins are much needed in the future especially for the studies
of gene-environment interactions on specific type 2 diabetes-related genetic
variants/traits and the lifestyle/behavioral factors in childhood/adolescent and adult life
such as obesity, physical activity, diet, smoking, alcohol consumption and others. The
83

studies of type 2 diabetes discordant twin pairs in MZ and DZ twins will provide further
understanding of the development/pathogenesis of type 2 diabetes and its relation to
individual genetic and/or environmental factors and the interaction of both factors while
genetic susceptibility is controlled totally and/or partially. The future studies will help to
evaluate whether the relation between exposures (genetic/environmental factors, the
interaction of both) and outcome is causal.
The future plan following this current study is to initiate the preparation/discussion of a
matched case-control study nested in the original California Twin Program (CTP) to
follow up type 2 diabetes-discordant pairs of DZ and MZ twins prospectively to obtain
the physician-confirmed diabetes diagnoses, test values indicating insulin resistance,
biological specimen for genetic screening, lifestyle/behavioral factors in
childhood/adolescence, and adult life, and etc to further validate this current study results
and to provide an insight on the quantitative extent of genetic influence, environmental
influence, and influence of both aspects in the development of type 2 diabetes.

84

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Abstract (if available)

Abstract Previous studies have shown that the development of type 2 diabetes mellitus (Type 2 DM) is affected by multiple genetic and environmental factors. Numerous family and twin studies have provided evidence showing that the risk of Type 2 DM is elevated among families and twins if another family member or co-twin has the disease [Serjeantson et al. 1983]. The familial aggregation [Cauchi et al. 2008, Watanabe et al. 2007, Yi et al. 2005, Prunier et al. 2004, Prestwich et al. 2007, Cossu et al. 1999, McLin et al. 2007] and higher concordance rate found in monozygotic (MZ) twins compares to dizygotic (DZ) twins [Fehmann et al. 1995, Florez et al. 2006, Lyssenko et al. 2007, Shu et al. 2008, Cauchi et al. 2008, Chimieti et al. 2005, Sladek et al. 2007, Steinthorsdottir et al. 2007, Chimienti et al. 2006, Taylor et al. 2005, Simon et al. 2001, Raz et al. 1989] points to the possibility of genetic influence in the development of Type 2 DM. However, familial aggregation and twin concordance support not only a genetic factor in the development of Type 2 DM but also the possibility of shared environmental influences. Despite the attempts by various types of studies including family studies [Cauchi et al. 2008, Watanabe et al. 2007, Yi et al. 2005, Prunier et al. 2004, Prestwich et al. 2007, Cossu et al. 1999, McLin et al. 2007], twin studies [Fehmann et al. 1995, Florez et al. 2006, Lyssenko et al. 2007, Shu et al. 2008, Cauchi et al. 2008, Chimieti et al. 2005, Sladek et al. 2007, Steinthorsdottir et al. 2007, Chimienti et al. 2006, Taylor et al. 2005, Simon et al. 2001, Raz et al. 1989), observational studies (Martin et al. 1992, Assd et al. 1991, Ling et al. 2004, Carlsson et al. 2005, Winckler et al. 2007, Deeb et al. 1998, Altshuler et al. 2000, Yasuda et al. 2008, Unoki et al. 2008, Sandhu et al. 2007], genetic linkage studies [Pearson et al. 2007, Lyssenko et al. 2005, Ahlzen et al. 2008, Weedon et al. 2006, Wasserfall et al. 2006, Ahmann et al. 2006,

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Creator Tsai, Pei-Jung (author)

Core Title The development of type 2 diabetes mellitus (type 2 DM) in California twins

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Epidemiology

Degree Conferral Date 2011-05

Publication Date 05/04/2011

Defense Date 03/22/2011

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Tag adjusted odds ratio,adjusted OR,body mass index,disease discordant probability,dizygotic,DZ,genetic susceptibility,monozygotic,MZ,OAI-PMH Harvest,Smoking,type 2 diabetes,type 2 diabetes mellitus

Place Name California (states), USA (countries)

Language English

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Advisor Mack, Thomas M. (committee chair), Buchanan, Thomas A. (committee member), Cozen, Wendy (committee member), Hamilton, Ann S. (committee member), Watanabe, Richard M. (committee member)

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