Metabolic syndrome: from epidemiology to systems biology
Aldons J. Lusis*,‡, Alan D. Attie§, and Karen Reue‡
* Department of Medicine and Department of Microbiology, Immunology and Molecular Genetics,
David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
‡ Department of Human Genetics, University of California, Los Angeles, California 90095, USA
§ Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706, USA
* Department of Medicine and Department of Microbiology, Immunology and Molecular Genetics,
David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
‡ Department of Human Genetics, University of California, Los Angeles, California 90095, USA
§ Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706, USA
Abstract
Metabolic syndrome (MetSyn) is a group of metabolic conditions that occur together and promote
the development of cardiovascular disease (CVD) and diabetes. Recent genome-wide association
studies have identified several novel susceptibility genes for MetSyn traits, and studies in rodent
models have provided important molecular insights. However, as yet, only a small fraction of the
genetic component is known. Systems-based approaches that integrate genomic, molecular and
physiological data are complementing traditional genetic and biochemical approaches to more fully
address the complexity of MetSyn.
The common forms of type 2 diabetes and cardiovascular disease (CVD) are strongly
associated with various common metabolic disturbances, including abdominal obesity, insulin
resistance, dyslipidaemias and elevated blood pressure. Since Reaven noted in 1988 that insulin
resistance could underlie much of this clustering1, a large body of work has supported the
concept that these metabolic traits exhibit causal interactions and common etiologies (BOX 1).
Metabolic syndrome (MetSyn) is a group of metabolic conditions that occur together and promote
the development of cardiovascular disease (CVD) and diabetes. Recent genome-wide association
studies have identified several novel susceptibility genes for MetSyn traits, and studies in rodent
models have provided important molecular insights. However, as yet, only a small fraction of the
genetic component is known. Systems-based approaches that integrate genomic, molecular and
physiological data are complementing traditional genetic and biochemical approaches to more fully
address the complexity of MetSyn.
The common forms of type 2 diabetes and cardiovascular disease (CVD) are strongly
associated with various common metabolic disturbances, including abdominal obesity, insulin
resistance, dyslipidaemias and elevated blood pressure. Since Reaven noted in 1988 that insulin
resistance could underlie much of this clustering1, a large body of work has supported the
concept that these metabolic traits exhibit causal interactions and common etiologies (BOX 1).
The clustering is now known as metabolic syndrome (MetSyn), although there is
considerable controversy within the epidemiology and genetics communities as to whether
MetSyn exists as a separate syndrome and how it should be defined (BOX 2). By applying a
widely used clinical definition, individuals affected with MetSyn have at least a fivefold
increased risk of type 2 diabetes and a twofold increased risk of CVD2.
considerable controversy within the epidemiology and genetics communities as to whether
MetSyn exists as a separate syndrome and how it should be defined (BOX 2). By applying a
widely used clinical definition, individuals affected with MetSyn have at least a fivefold
increased risk of type 2 diabetes and a twofold increased risk of CVD2.
Box 1
Interactions of MetSyn traits in diabetes and cardiovascular diseases
There are many interactions between components of metabolic syndrome (MetSyn) traits
associated with diabetes and cardiovascular disease (CVD). These are shown in the figure
and described below.
1. The response to calorific excess is influenced by genetic factors, as are other
interactions2,7,75,80.
2. Effects of exercise include increased lipoprotein lipase, reduced plasma
triglycerides, elevated high-density lipoprotein (HDL), improved glucose
tolerance, heart function and oxygen uptake, and lower blood pressure2.
3. Excess lipids are stored largely in fat, but calorific excess can also promote storage
by ectopic tissues, such as muscle and pancreatic beta cells, leading to a form of
toxicity57,94.
4. Excess fat influences lipoprotein levels: for example, in obese individuals
increased flux of free fatty acids from fat to the liver might stimulate production
of triglyceride-rich lipoproteins7.
5. Excessive fat results in a proinflammatory state owing to altered production of
inflammatory (for example, leptin) and anti-inflammatory (for example
adiponectin) mediators and the recruitment of macrophages to adipose34,35,71,72.
6. Excessive adipose contributes to insulin resistance in part through increased
release of free fatty acids and cytokines (for example, tumour necrosis factor a),
and decreased production of adiponectin, an insulin sensitizer9,34,35,62,63.
7. Ectopic fat overload can result in dysfunction of cardiac muscle and pancreatic
beta cells57,94,98.
8. Obesity can lead to decreased lipolysis of triglyceride-rich lipoproteins mediated
by decreased lipoprotein lipase and to increased catabolism of HDL mediated by
increased hepatic lipase7.
9. Obesity causes overproduction of very low-density lipoprotein (VLDL) by the
liver. Also, insulin resistance results in an inability to suppress hepatic glucose
production7.
10. Overproduction of triglyceride-rich lipoproteins, mediated in part by hepatic
insulin resistance, is a common feature of MetSyn. Elevated fatty acids are also
implicated in insulin resistance116.
11. Inter-organ interactions involving cytokines and inflammatory mediators
produced by adipose tissue, liver or other tissues seem to contribute to insulin
resistance6.
12. Obesity and insulin resistance can predict future development of hypertension.
This might occur by activation of the sympathetic nervous system as well as the
renin–angiotensin system. The succinate receptor GPR91 may explain the link
between elevated glucose and renin117.
13. Insulin resistance almost always precedes type 2 diabetes. In diabetes the ability
of beta cells to compensate for insulin resistance by both metabolic and mass
changes is impaired3–7,9,58.
14. HDL levels are inversely related to CVD. HDL functions in transport of cholesterol
from peripheral tissues to the liver for removal in bile and has anti-inflammatory
properties7,91.
15. Triglyceride levels are a strong predictor of CVD, but the basis of this is still
unclear2.
16. Obesity may exert direct and indirect effects on CVD, as proinflammatory and
prothrombotic factors are produced by visceral fat, and a number of adipokines
seem to contribute to CVD risk7
17. Elevated blood pressure has direct adverse effects on arteries, arterioles and the
heart, and is strongly associated with stroke, myocardial infarction and myocardial
hypertrophy4.
18. Decreased insulin production and increased insulin resistance result in elevated
glucose levels, which can be toxic and proinflammatory92,93.
19. Poor cardiac function can combine with other MetSyn effects to cause heart failure.
20. Elevated glucose levels can contribute to atherosclerosis, for example via advanced
glycation products92,93.
21. Probable causes of endothelial dysfunction include elevated glucose, altered lipid
profiles, obesity-associated pro-inflammatory molecules such as interleukin-6,
and, possibly, oxidative stress7.
22. Physical disruption or endothelial denudation account for about 75% and 25%,
respectively, of myocardial infarction118.
23. Damage to the heart due to myocardial infarction promotes changes leading to
heart failure.
24. Microvascular disease involves damage of the small arteries and can affect the
functioning of the kidneys, brain and heart.
Interactions of MetSyn traits in diabetes and cardiovascular diseases
There are many interactions between components of metabolic syndrome (MetSyn) traits
associated with diabetes and cardiovascular disease (CVD). These are shown in the figure
and described below.
1. The response to calorific excess is influenced by genetic factors, as are other
interactions2,7,75,80.
2. Effects of exercise include increased lipoprotein lipase, reduced plasma
triglycerides, elevated high-density lipoprotein (HDL), improved glucose
tolerance, heart function and oxygen uptake, and lower blood pressure2.
3. Excess lipids are stored largely in fat, but calorific excess can also promote storage
by ectopic tissues, such as muscle and pancreatic beta cells, leading to a form of
toxicity57,94.
4. Excess fat influences lipoprotein levels: for example, in obese individuals
increased flux of free fatty acids from fat to the liver might stimulate production
of triglyceride-rich lipoproteins7.
5. Excessive fat results in a proinflammatory state owing to altered production of
inflammatory (for example, leptin) and anti-inflammatory (for example
adiponectin) mediators and the recruitment of macrophages to adipose34,35,71,72.
6. Excessive adipose contributes to insulin resistance in part through increased
release of free fatty acids and cytokines (for example, tumour necrosis factor a),
and decreased production of adiponectin, an insulin sensitizer9,34,35,62,63.
7. Ectopic fat overload can result in dysfunction of cardiac muscle and pancreatic
beta cells57,94,98.
8. Obesity can lead to decreased lipolysis of triglyceride-rich lipoproteins mediated
by decreased lipoprotein lipase and to increased catabolism of HDL mediated by
increased hepatic lipase7.
9. Obesity causes overproduction of very low-density lipoprotein (VLDL) by the
liver. Also, insulin resistance results in an inability to suppress hepatic glucose
production7.
10. Overproduction of triglyceride-rich lipoproteins, mediated in part by hepatic
insulin resistance, is a common feature of MetSyn. Elevated fatty acids are also
implicated in insulin resistance116.
11. Inter-organ interactions involving cytokines and inflammatory mediators
produced by adipose tissue, liver or other tissues seem to contribute to insulin
resistance6.
12. Obesity and insulin resistance can predict future development of hypertension.
This might occur by activation of the sympathetic nervous system as well as the
renin–angiotensin system. The succinate receptor GPR91 may explain the link
between elevated glucose and renin117.
13. Insulin resistance almost always precedes type 2 diabetes. In diabetes the ability
of beta cells to compensate for insulin resistance by both metabolic and mass
changes is impaired3–7,9,58.
14. HDL levels are inversely related to CVD. HDL functions in transport of cholesterol
from peripheral tissues to the liver for removal in bile and has anti-inflammatory
properties7,91.
15. Triglyceride levels are a strong predictor of CVD, but the basis of this is still
unclear2.
16. Obesity may exert direct and indirect effects on CVD, as proinflammatory and
prothrombotic factors are produced by visceral fat, and a number of adipokines
seem to contribute to CVD risk7
17. Elevated blood pressure has direct adverse effects on arteries, arterioles and the
heart, and is strongly associated with stroke, myocardial infarction and myocardial
hypertrophy4.
18. Decreased insulin production and increased insulin resistance result in elevated
glucose levels, which can be toxic and proinflammatory92,93.
19. Poor cardiac function can combine with other MetSyn effects to cause heart failure.
20. Elevated glucose levels can contribute to atherosclerosis, for example via advanced
glycation products92,93.
21. Probable causes of endothelial dysfunction include elevated glucose, altered lipid
profiles, obesity-associated pro-inflammatory molecules such as interleukin-6,
and, possibly, oxidative stress7.
22. Physical disruption or endothelial denudation account for about 75% and 25%,
respectively, of myocardial infarction118.
23. Damage to the heart due to myocardial infarction promotes changes leading to
heart failure.
24. Microvascular disease involves damage of the small arteries and can affect the
functioning of the kidneys, brain and heart.
Box 2
A short history of metabolic syndrome
In 1988 Gerald Reaven explained how insulin resistance and the compensatory
hyperinsulinaemia could lead to diverse metabolic abnormalities as well as type 2 diabetes
and cardiovascular disease (CVD)1. Reaven originally termed the clustering of
abnormalities ‘syndrome X’ and it has also been called ‘insulin resistance syndrome’, but
now it is commonly termed metabolic syndrome (MetSyn). The original definition of
MetSyn component traits included impaired glucose tolerance, hyperinsulinaemia, reduced
high-density lipoprotein (HDL) cholesterol, increased very low-density lipoprotein (VLDL)
triglycerides and arterial hypertension, and it was suggested that insulin resistance was the
underlying cause of this clustering1. Over the past two decades, it has become clear that
these traits are not tied together exclusively by insulin resistance, although it is clearly a
key component1,38,119. Additional definitions of MetSyn have been promoted, including
alternative component traits such as central obesity (increased waist circumference),
microalbuminuria and prothrombotic state (reviewed in REFS 120–122). Although the
specific numbers obtained vary depending on the definition used123, based on the adult
treatment panel III criteria2, MetSyn is incredibly prevalent worldwide; for example, in the
United States about 25% of adults over age 20 and 40% over age 60 exhibit MetSyn124.
The controversy over a specific definition of MetSyn has complicated both clinical and
genetic studies, and raises the question of whether there is value in studying the syndrome
as a specific entity beyond that of studying the component traits125. However, there is no
question that the concept of MetSyn has made both physicians and the public more aware
of important interactions, such as the link between obesity and disease and the beneficial
consequences of physical exercise for all MetSyn traits126. The concept of MetSyn has also
greatly stimulated research into these relationships.
A short history of metabolic syndrome
In 1988 Gerald Reaven explained how insulin resistance and the compensatory
hyperinsulinaemia could lead to diverse metabolic abnormalities as well as type 2 diabetes
and cardiovascular disease (CVD)1. Reaven originally termed the clustering of
abnormalities ‘syndrome X’ and it has also been called ‘insulin resistance syndrome’, but
now it is commonly termed metabolic syndrome (MetSyn). The original definition of
MetSyn component traits included impaired glucose tolerance, hyperinsulinaemia, reduced
high-density lipoprotein (HDL) cholesterol, increased very low-density lipoprotein (VLDL)
triglycerides and arterial hypertension, and it was suggested that insulin resistance was the
underlying cause of this clustering1. Over the past two decades, it has become clear that
these traits are not tied together exclusively by insulin resistance, although it is clearly a
key component1,38,119. Additional definitions of MetSyn have been promoted, including
alternative component traits such as central obesity (increased waist circumference),
microalbuminuria and prothrombotic state (reviewed in REFS 120–122). Although the
specific numbers obtained vary depending on the definition used123, based on the adult
treatment panel III criteria2, MetSyn is incredibly prevalent worldwide; for example, in the
United States about 25% of adults over age 20 and 40% over age 60 exhibit MetSyn124.
The controversy over a specific definition of MetSyn has complicated both clinical and
genetic studies, and raises the question of whether there is value in studying the syndrome
as a specific entity beyond that of studying the component traits125. However, there is no
question that the concept of MetSyn has made both physicians and the public more aware
of important interactions, such as the link between obesity and disease and the beneficial
consequences of physical exercise for all MetSyn traits126. The concept of MetSyn has also
greatly stimulated research into these relationships.
The MetSyn traits are highly heritable — they are shared among relatives according to the
degree of relatedness. Recent genome-wide association (GWA) studies have clearly shown
that these traits are the result of combinations of common and rare genetic variants, each of
which contributes to a tiny fraction of risk. Traditional genetic approaches have identified some
key genes and highlighted metabolic pathways that are dysregulated in MetSyn. However, it
is becoming clear that new strategies, such as systems approaches that integrate the
contributions of genetic variations in numerous genes simultaneously, will be required to
elucidate the genetic factors underlying MetSyn in the majority of affected individuals (FIG.
1).
The MetSyn literature is vast and, in this Review, we have not attempted to be comprehensive.
Many central issues, such as the relationships between insulin resistance, beta-cell function
and the onset of diabetes, have been extensively reviewed elsewhere3–7 and are not addressed
in detail here. Rather, we have focused on the current important research areas and challenges
that are being addressed with genetic approaches. We first provide an overview of the genetic
and environmental factors contributing to MetSyn. Second, we discuss some mostly
unexplored but crucially important aspects, including sexspecific effects and maternal
nutrition. Third, we outline current understanding of the underlying mechanisms, which is
based mainly on studies in rodent models. Finally, we discuss the complexity of MetSyn,
arguing that GWA studies and traditional biochemical approaches will not be capable of fully
addressing the epistasis and other interactions that predominate in MetSyn, and that more
integrative, systems-based approaches will be essential.
Genetic and environmental factors
All MetSyn traits are strongly influenced by genetic factors. Most have heritabilities above
40% and a few, such as obesity and high-density lipoprotein (HDL) levels, have heritabilities
as high as 70% in some studies4. But heritability estimates are approximate and they generally
make certain assumptions, such as the absence of gene– environment, gene–sex and gene–gene
interactions as these are difficult to dissect in human populations. Also, estimates are specific
for the particular population studied and will reflect both the diversity of the population and
the diversity of the environment8.
Until recently, our understanding of the genetics of MetSyn came largely from studies of
Mendelian traits in humans or of biochemically defined candidate genes. Although these
studies were enormously informative in providing molecular insights into homeostatic
mechanisms, they have not explained how genes interact with each other and with the
environment.
Studies that began in the early 1990s to identify genes contributing to the common forms of
MetSyn traits using linkage analysis were only modestly successful. This was primarily due
to the low power and poor resolution of nonparametric linkage analyses, as well as the
unexpected complexity of the traits. Whereas linkage analysis of the genes contributing to the
traits in rats and mice is straightforward, at least for those genes contributing more than a few
percent of the variance of the trait, resolution is very poor and loci generally contain 100 or
more genes. As a consequence, successes in identifying genes underlying quantitative trait loci
(QTLs) have been few.
GWA studies became feasible following the completion of the human genome sequence, the
cataloguing of common variations in human populations and the development of improved
SNP genotyping technologies. Association approaches are more powerful and have much
better resolution than linkage approaches. Several large studies of traits relevant to MetSyn
have been reported (BOX 3); these have confirmed a number of genes previously identified
through candidate gene approaches and have identified many novel genes or loci (reviewed in
REFS 9–11). These include: two common variants that affect fasting glucose levels
(glucokinase (hexokinase 4) regulator (GCKR), and a genomic region containing
glucose6phosphatase, catalytic, 2 (G6PC2) and ATP binding cassette, subfamily B, member
11 (ABCB11)); two obesity (that is, adiposity) variants (fat mass and obesity associated
(FTO) and melanocortin 4 receptor (MC4R)); 19 type 2 diabetes loci; and many triglyceride,
HDLcholesterol and lowdensity lipoprotein (LDL)-cholesterol loci12–24. None of the genes
identified affect the entire spectrum of MetSyn traits, although some influence several of them
(BOX 3). For example, studies of FTO show that, although its primary effect is on adiposity,
it has secondary effects on insulin sensitivity, adipokine levels and resting metabolic rate25,
26. Additional genetic studies, identification of rare functional variants by high-throughput
sequencing and analysis of copy number variation should add to our knowledge24,27–29.
All MetSyn traits are strongly influenced by genetic factors. Most have heritabilities above
40% and a few, such as obesity and high-density lipoprotein (HDL) levels, have heritabilities
as high as 70% in some studies4. But heritability estimates are approximate and they generally
make certain assumptions, such as the absence of gene– environment, gene–sex and gene–gene
interactions as these are difficult to dissect in human populations. Also, estimates are specific
for the particular population studied and will reflect both the diversity of the population and
the diversity of the environment8.
Until recently, our understanding of the genetics of MetSyn came largely from studies of
Mendelian traits in humans or of biochemically defined candidate genes. Although these
studies were enormously informative in providing molecular insights into homeostatic
mechanisms, they have not explained how genes interact with each other and with the
environment.
Studies that began in the early 1990s to identify genes contributing to the common forms of
MetSyn traits using linkage analysis were only modestly successful. This was primarily due
to the low power and poor resolution of nonparametric linkage analyses, as well as the
unexpected complexity of the traits. Whereas linkage analysis of the genes contributing to the
traits in rats and mice is straightforward, at least for those genes contributing more than a few
percent of the variance of the trait, resolution is very poor and loci generally contain 100 or
more genes. As a consequence, successes in identifying genes underlying quantitative trait loci
(QTLs) have been few.
GWA studies became feasible following the completion of the human genome sequence, the
cataloguing of common variations in human populations and the development of improved
SNP genotyping technologies. Association approaches are more powerful and have much
better resolution than linkage approaches. Several large studies of traits relevant to MetSyn
have been reported (BOX 3); these have confirmed a number of genes previously identified
through candidate gene approaches and have identified many novel genes or loci (reviewed in
REFS 9–11). These include: two common variants that affect fasting glucose levels
(glucokinase (hexokinase 4) regulator (GCKR), and a genomic region containing
glucose6phosphatase, catalytic, 2 (G6PC2) and ATP binding cassette, subfamily B, member
11 (ABCB11)); two obesity (that is, adiposity) variants (fat mass and obesity associated
(FTO) and melanocortin 4 receptor (MC4R)); 19 type 2 diabetes loci; and many triglyceride,
HDLcholesterol and lowdensity lipoprotein (LDL)-cholesterol loci12–24. None of the genes
identified affect the entire spectrum of MetSyn traits, although some influence several of them
(BOX 3). For example, studies of FTO show that, although its primary effect is on adiposity,
it has secondary effects on insulin sensitivity, adipokine levels and resting metabolic rate25,
26. Additional genetic studies, identification of rare functional variants by high-throughput
sequencing and analysis of copy number variation should add to our knowledge24,27–29.
Box 3
Human genome-wide association studies: a view of the genetic architecture
of MetSyn
Several genome-wide association (GWA) studies relevant to metabolic syndrome
(MetSyn), type 2 diabetes and coronary artery disease have confirmed candidate gene
associations and have identified a number of novel genes and loci (discussed in the text and
reviewed in REFS 9–11). Examples include:
• Melanocortin 4 receptor, MC4R: this gene was identified in Asian-Indian and
European populations for several MetSyn traits17, 127. Rare MC4R loss-offunction
variants have previously been associated with hyperphagia and childhood
obesity, and experimental studies have identified it as a key regulator of energy
balance.
• Fat mass and obesity associated, FTO: two GWA studies of Europeans have
associated FTO with body mass index14, 23. Recent studies in rodents suggest that
FTO might be co-regulated with an adjacent gene, FTM, and that it exhibits
phenotypic overlap with Bardet-Biedl syndrome128.
• MLX interacting protein-like, MLXIPL: in European and Indian–Asian
populations this gene is linked to plasma triglyceride levels129. Its protein product
coordinates transcriptional regulation of enzymes that channel glycolytic endproducts
into lipogenesis and energy storage.
• Transcription factor 7-like 2, T-cell specific, HMG-box, TCF7L2: this is one of
19 susceptibility genes for type 2 diabetes. Although originally identified by
genetic linkage followed by traditional genetic association130, this association was
confirmed by GWA studies24. Many of the type 2 diabetes genes, including
TCF7L2, seem to affect pancreatic beta-cell function.
As yet, no genetic factors that encompass all MetSyn traits have been identified. This might
simply reflect the lack of power of the analyses, as genes perturbing individual pathways
might indirectly contribute to traits such as lipid levels and blood pressure. Some traits,
such as blood pressure, have few or no loci that achieve genome-wide significance, and for
the others the identified loci explain less than 10% of the variance of the trait. As most
MetSyn traits have heritabilities of approximately 50%, genes detectable above the noise
of GWA studies explain only a small fraction of the genetic component. This is illustrated
in a hypothetical distribution of genes contributing to MetSyn (see figure). Genes identified
thus far by GWA studies (shown at left) tend to be those exerting the largest effects, but
account for only ~5–10% of the trait variance. The majority of remaining genes (so called
‘dark matter’) will be more difficult to identify owing to their modest effects on MetSyn
traits, complex interactions and rare variations.
Human genome-wide association studies: a view of the genetic architecture
of MetSyn
Several genome-wide association (GWA) studies relevant to metabolic syndrome
(MetSyn), type 2 diabetes and coronary artery disease have confirmed candidate gene
associations and have identified a number of novel genes and loci (discussed in the text and
reviewed in REFS 9–11). Examples include:
• Melanocortin 4 receptor, MC4R: this gene was identified in Asian-Indian and
European populations for several MetSyn traits17, 127. Rare MC4R loss-offunction
variants have previously been associated with hyperphagia and childhood
obesity, and experimental studies have identified it as a key regulator of energy
balance.
• Fat mass and obesity associated, FTO: two GWA studies of Europeans have
associated FTO with body mass index14, 23. Recent studies in rodents suggest that
FTO might be co-regulated with an adjacent gene, FTM, and that it exhibits
phenotypic overlap with Bardet-Biedl syndrome128.
• MLX interacting protein-like, MLXIPL: in European and Indian–Asian
populations this gene is linked to plasma triglyceride levels129. Its protein product
coordinates transcriptional regulation of enzymes that channel glycolytic endproducts
into lipogenesis and energy storage.
• Transcription factor 7-like 2, T-cell specific, HMG-box, TCF7L2: this is one of
19 susceptibility genes for type 2 diabetes. Although originally identified by
genetic linkage followed by traditional genetic association130, this association was
confirmed by GWA studies24. Many of the type 2 diabetes genes, including
TCF7L2, seem to affect pancreatic beta-cell function.
As yet, no genetic factors that encompass all MetSyn traits have been identified. This might
simply reflect the lack of power of the analyses, as genes perturbing individual pathways
might indirectly contribute to traits such as lipid levels and blood pressure. Some traits,
such as blood pressure, have few or no loci that achieve genome-wide significance, and for
the others the identified loci explain less than 10% of the variance of the trait. As most
MetSyn traits have heritabilities of approximately 50%, genes detectable above the noise
of GWA studies explain only a small fraction of the genetic component. This is illustrated
in a hypothetical distribution of genes contributing to MetSyn (see figure). Genes identified
thus far by GWA studies (shown at left) tend to be those exerting the largest effects, but
account for only ~5–10% of the trait variance. The majority of remaining genes (so called
‘dark matter’) will be more difficult to identify owing to their modest effects on MetSyn
traits, complex interactions and rare variations.
The loci identified in GWA studies frequently contain several genes in strong linkage
disequilibrium, and biochemical or animal model studies might be required to definitively
identify which gene is causal. One promising approach to validate susceptibility genes involves
network modelling, which allows genes of unknown function to be related to known pathways
or clinical traits. For example, the integration of human and mouse genotypic and expression
data suggested that sortilin 1 (SORT1) and cadherin EGF LAG sevenpass G-type receptor 2
(CELSR2) are susceptibility genes for CVD and hyperlipidaemia30.
The loci identified thus far from GWA studies explain less than 10% of the population variance
of MetSyn traits. Given the high heritabilities of MetSyn traits, it seems that the GWA study
results reported so far have mapped a tiny fraction of their genetic components. This raises the
possibility that MetSyn is underpinned by hundreds of genes, each with modest effects, and
by many rare mutations not detected in GWA studies (BOX 2). Genes with such modest effects
will be difficult to study using standard genetic approaches, and genetic heterogeneity,
interactions and ethnic differences will complicate analyses. Recent sequencing studies
designed to identify rare genetic variants involved in common disorders suggest that these are
likely to contribute significantly to MetSyn traits (for example, REFS 27,28).
A central tenet of MetSyn has been the ‘thrifty gene’ hypothesis — the notion that the repeated
famines in human history have selected for alleles that result in obesity during times of plentiful
food. Thus, when a famine occurs those individuals with excess fat would be most likely to
survive. However, recent data indicating that death from starvation results primarily from
infection rather than depletion of fat stores has put the hypothesis into question31.
Environmental influences also play a major part in MetSyn: a high-calorie diet and a sedentary
life-style are primary environmental contributors (BOX 1). Environmental factors are difficult
to study in humans. Even if diet and exercise could be accurately assessed, interactions with
genetic factors would be difficult to study because no two humans, with the exception of
identical twins, share the same genetic background. Consequently, few human studies have as
yet attempted to tackle gene–gene and gene–environment interactions, and have focused
instead on single candidate genes. Whether classical genetic and molecular biology approaches
can address these complex interactions is unclear. Alternative systems-based approaches are
discussed later in this Review.
disequilibrium, and biochemical or animal model studies might be required to definitively
identify which gene is causal. One promising approach to validate susceptibility genes involves
network modelling, which allows genes of unknown function to be related to known pathways
or clinical traits. For example, the integration of human and mouse genotypic and expression
data suggested that sortilin 1 (SORT1) and cadherin EGF LAG sevenpass G-type receptor 2
(CELSR2) are susceptibility genes for CVD and hyperlipidaemia30.
The loci identified thus far from GWA studies explain less than 10% of the population variance
of MetSyn traits. Given the high heritabilities of MetSyn traits, it seems that the GWA study
results reported so far have mapped a tiny fraction of their genetic components. This raises the
possibility that MetSyn is underpinned by hundreds of genes, each with modest effects, and
by many rare mutations not detected in GWA studies (BOX 2). Genes with such modest effects
will be difficult to study using standard genetic approaches, and genetic heterogeneity,
interactions and ethnic differences will complicate analyses. Recent sequencing studies
designed to identify rare genetic variants involved in common disorders suggest that these are
likely to contribute significantly to MetSyn traits (for example, REFS 27,28).
A central tenet of MetSyn has been the ‘thrifty gene’ hypothesis — the notion that the repeated
famines in human history have selected for alleles that result in obesity during times of plentiful
food. Thus, when a famine occurs those individuals with excess fat would be most likely to
survive. However, recent data indicating that death from starvation results primarily from
infection rather than depletion of fat stores has put the hypothesis into question31.
Environmental influences also play a major part in MetSyn: a high-calorie diet and a sedentary
life-style are primary environmental contributors (BOX 1). Environmental factors are difficult
to study in humans. Even if diet and exercise could be accurately assessed, interactions with
genetic factors would be difficult to study because no two humans, with the exception of
identical twins, share the same genetic background. Consequently, few human studies have as
yet attempted to tackle gene–gene and gene–environment interactions, and have focused
instead on single candidate genes. Whether classical genetic and molecular biology approaches
can address these complex interactions is unclear. Alternative systems-based approaches are
discussed later in this Review.
Important gaps in the understanding of MetSyn
Sex differences and MetSyn susceptibility
Men and women differ in susceptibility to MetSyn and its components, including obesity,
insulin resistance, CVD and hypertension32. Differences between males and females in insulin
resistance seem to be related to differences in the anatomical distribution of fat5,33,34. Males
tend to have more visceral fat, which is linked to insulin resistance, whereas females typically
carry more subcutaneous fat.
Several hypotheses have been put forward to explain the link between visceral fat and insulin
resistance. One possibility is that the molecular characteristics of visceral versus subcutaneous
fat differ, leading to increased visceral adiposetissue lipolysis, glucocorticoid receptor activity
and inflammatory cytokine secretion, and to reduced secretion of the insulin sensitizing
adipokine, adiponectin34,35. Alternatively, the physical location of the Mechanism visceral-fat
compartment might allow release of free fatty acids, inflammatory cytokines and other adipose
tissue metabolites directly into the portal circulation33. Recent gene expression profiling
studies suggest that there are intrinsic molecular differences between visceral and subcutaneous
adipose tissue depots in both humans and mice36. Furthermore, fat transplantation studies in
the mouse revealed that transfer of subcutaneous adipose tissue to an intra-abdominal
compartment led to an overall decrease in body fat and improved glucose homeostasis37,38.
These results indicate that metabolic differences exist between fat deposited at subcutaneous
versus visceral sites, at least in mice.
These studies raise the possibility that visceral and subcutaneous fat depots might be derived
from distinct progenitor populations35. This is consistent with observations in human
lipodystrophies caused by rare gene mutations. Individuals with Dunnigan-type lipodystrophy
exhibit a dramatic loss of subcutaneous adipose tissue, but normal or increased fat accumulation
in visceral, neck and facial areas39, and Hottentot (Khoisan) women show marked
accumulation of gluteal–femoral adipose tissue40.
Given the important metabolic differences between visceral and subcutaneous fat depots, it is
important to understand the genetic basis for their occurrence in males and females. Differences
in levels of gonadal hormones are undoubtedly important. For example, the accumulation of
excess abdominal adipose tissue in males is associated with low levels of gonadal androgen,
and the reduced levels of oestrogen, progestins and androgens that occur in menopause are
associated with increased central fat distribution in women41,42. Male and female mice exhibit
marked gene expression differences in metabolic tissues, such as adipose tissue and liver43.
Genetic studies in mice have revealed striking interactions between sex and adiposity, and
some alleles affect adiposity in opposite directions between males and females. However,
factors other than gonadal hormones also seem to have a role, as not all sex differences in
adipose tissue gene expression are abolished by gonadectomy. For example, threefold higher
adiponectin, leptin and resistin mRnA levels persist in female compared with male mice 3
weeks after castration at 9 weeks of age44. Differences occurring between males and females
after gonadectomy can be attributed either to differences in previous steroid hormone history
or to genetic differences that are due to the dosage of X and Y chromosome genes. Indeed, sex
differences in embryonic growth of humans and mice are caused by direct effects of genes
located on the sex chromosomes, as these differences precede the differentiation of
gonads45. The contribution of sex-chromosome effects to obesity and MetSyn could be
examined in, for example, mice that are engineered so that sex chromosome complement
segregates independently of male and female gonadal sex46. By comparing normal male and
female mice with animals that develop male gonads despite having an XX chromosome
complement or female gonads despite an XY chromosome complement, it might be possible
to assign MetSyn traits to hormonal or genetic factors.
Sex differences and MetSyn susceptibility
Men and women differ in susceptibility to MetSyn and its components, including obesity,
insulin resistance, CVD and hypertension32. Differences between males and females in insulin
resistance seem to be related to differences in the anatomical distribution of fat5,33,34. Males
tend to have more visceral fat, which is linked to insulin resistance, whereas females typically
carry more subcutaneous fat.
Several hypotheses have been put forward to explain the link between visceral fat and insulin
resistance. One possibility is that the molecular characteristics of visceral versus subcutaneous
fat differ, leading to increased visceral adiposetissue lipolysis, glucocorticoid receptor activity
and inflammatory cytokine secretion, and to reduced secretion of the insulin sensitizing
adipokine, adiponectin34,35. Alternatively, the physical location of the Mechanism visceral-fat
compartment might allow release of free fatty acids, inflammatory cytokines and other adipose
tissue metabolites directly into the portal circulation33. Recent gene expression profiling
studies suggest that there are intrinsic molecular differences between visceral and subcutaneous
adipose tissue depots in both humans and mice36. Furthermore, fat transplantation studies in
the mouse revealed that transfer of subcutaneous adipose tissue to an intra-abdominal
compartment led to an overall decrease in body fat and improved glucose homeostasis37,38.
These results indicate that metabolic differences exist between fat deposited at subcutaneous
versus visceral sites, at least in mice.
These studies raise the possibility that visceral and subcutaneous fat depots might be derived
from distinct progenitor populations35. This is consistent with observations in human
lipodystrophies caused by rare gene mutations. Individuals with Dunnigan-type lipodystrophy
exhibit a dramatic loss of subcutaneous adipose tissue, but normal or increased fat accumulation
in visceral, neck and facial areas39, and Hottentot (Khoisan) women show marked
accumulation of gluteal–femoral adipose tissue40.
Given the important metabolic differences between visceral and subcutaneous fat depots, it is
important to understand the genetic basis for their occurrence in males and females. Differences
in levels of gonadal hormones are undoubtedly important. For example, the accumulation of
excess abdominal adipose tissue in males is associated with low levels of gonadal androgen,
and the reduced levels of oestrogen, progestins and androgens that occur in menopause are
associated with increased central fat distribution in women41,42. Male and female mice exhibit
marked gene expression differences in metabolic tissues, such as adipose tissue and liver43.
Genetic studies in mice have revealed striking interactions between sex and adiposity, and
some alleles affect adiposity in opposite directions between males and females. However,
factors other than gonadal hormones also seem to have a role, as not all sex differences in
adipose tissue gene expression are abolished by gonadectomy. For example, threefold higher
adiponectin, leptin and resistin mRnA levels persist in female compared with male mice 3
weeks after castration at 9 weeks of age44. Differences occurring between males and females
after gonadectomy can be attributed either to differences in previous steroid hormone history
or to genetic differences that are due to the dosage of X and Y chromosome genes. Indeed, sex
differences in embryonic growth of humans and mice are caused by direct effects of genes
located on the sex chromosomes, as these differences precede the differentiation of
gonads45. The contribution of sex-chromosome effects to obesity and MetSyn could be
examined in, for example, mice that are engineered so that sex chromosome complement
segregates independently of male and female gonadal sex46. By comparing normal male and
female mice with animals that develop male gonads despite having an XX chromosome
complement or female gonads despite an XY chromosome complement, it might be possible
to assign MetSyn traits to hormonal or genetic factors.
Epigenetics and maternal nutrition
One potentially important environmental factor for MetSyn that has recently been
experimentally validated is the quality of fetal nutrition. Extensive epidemiological studies
originally revealed inverse correlations between birth weight and CVD47. Many additional
studies have confirmed these findings and led to the hypothesis that early life stressors, such
as poor maternal nutrition, maternal obesity and rapid postnatal weight gain, can programme
metabolic adaptations for survival in a nutrient poor environment48. Thus, if the actual postnatal
environment is not nutrient poor, such programming can lead to adult-onset MetSyn.
Subsequent studies in rats and mice have confirmed these associations and have revealed
evidence of both metabolic and structural programming48,49. For example, following
intrauterine growth retardation (IUGR) in rats, the affected off-spring showed impaired insulin
secretion and developed MetSyn traits with ageing50. Recently, studies in mice showed that
the offspring of mothers maintained on a restricted diet exhibited a premature leptin surge and
an increased density of hypothalamic nerve terminals49. CVD has also been associated with
maternal hypercholesterolaemia in humans51, rabbits and mice52, raising the possibility of
overlapping mechanisms. It should be noted that although the human studies could clearly
involve genetic contributions, this cannot be the case for the animal studies owing to the use
of inbred strains.
Recent studies suggest that epigenetic mechanisms might underlie intrauterine
programming53. One of the molecular phenotypes associated with the aforementioned IUGR
in rats is decreased expression of PDX1, a key transcription factor regulating pancreatic
development. Recently, reduced PDX1 activity was associated with alterations in histone
modification50. Similar findings were observed for the glucose transporter GLUT4 in the
muscle of IUGR rats54. Interestingly, some metabolic traits resulting from low birth weights
can be transmitted to subsequent generations, suggesting the possibility of epigenetic changes
maintained during meiosis, as is observed for the agouti coat-colour variants in mice53. It will
be of great interest to examine the effects of intrauterine programming using more global
techniques for monitoring gene expression and chromatin structure.
One potentially important environmental factor for MetSyn that has recently been
experimentally validated is the quality of fetal nutrition. Extensive epidemiological studies
originally revealed inverse correlations between birth weight and CVD47. Many additional
studies have confirmed these findings and led to the hypothesis that early life stressors, such
as poor maternal nutrition, maternal obesity and rapid postnatal weight gain, can programme
metabolic adaptations for survival in a nutrient poor environment48. Thus, if the actual postnatal
environment is not nutrient poor, such programming can lead to adult-onset MetSyn.
Subsequent studies in rats and mice have confirmed these associations and have revealed
evidence of both metabolic and structural programming48,49. For example, following
intrauterine growth retardation (IUGR) in rats, the affected off-spring showed impaired insulin
secretion and developed MetSyn traits with ageing50. Recently, studies in mice showed that
the offspring of mothers maintained on a restricted diet exhibited a premature leptin surge and
an increased density of hypothalamic nerve terminals49. CVD has also been associated with
maternal hypercholesterolaemia in humans51, rabbits and mice52, raising the possibility of
overlapping mechanisms. It should be noted that although the human studies could clearly
involve genetic contributions, this cannot be the case for the animal studies owing to the use
of inbred strains.
Recent studies suggest that epigenetic mechanisms might underlie intrauterine
programming53. One of the molecular phenotypes associated with the aforementioned IUGR
in rats is decreased expression of PDX1, a key transcription factor regulating pancreatic
development. Recently, reduced PDX1 activity was associated with alterations in histone
modification50. Similar findings were observed for the glucose transporter GLUT4 in the
muscle of IUGR rats54. Interestingly, some metabolic traits resulting from low birth weights
can be transmitted to subsequent generations, suggesting the possibility of epigenetic changes
maintained during meiosis, as is observed for the agouti coat-colour variants in mice53. It will
be of great interest to examine the effects of intrauterine programming using more global
techniques for monitoring gene expression and chromatin structure.
Mechanistic insights
We now have at least a partial understanding of how molecular links between obesity, insulin
resistance, dyslipidaemias and hypertension contribute to diabetes, atherosclerosis, heart
failure and stroke (BOX 1). But the complexity and heterogeneity of MetSyn have made
mechanistic studies in humans especially difficult, and most molecular details have come from
studies with experimental models, particularly rodents3,7,55–57. A large number of MetSyn
models, including naturally occurring, and dietarily and genetically induced, have been
developed in rats and in mice55,56. Now the mouse is more widely used owing to its more
advanced genetics and its ease of genetic manipulation.
Inbred strains of rodents provide a uniform genetic background, so the effects of genetic or
environmental perturbations can be inferred. Also, the effects of genetic modifiers can be
examined on the background of disease-causing alleles. Tools such as tissue-specific knockouts
have enabled examination of the contributions of individual tissues or cell types to MetSyn.
For example, although adipose tissue, pancreas and muscle have long been known to contribute
to MetSyn, studies in rodents have indicated that several additional tissues, including brain,
liver, intestine, kidney and haematopoietic cells, can have causal roles. Although genes and
metabolic pathways are highly conserved between rodents and humans, and rodent models are
particularly valuable for dissecting mechanistic interactions, human studies remain essential
for determining their relevance to disease. For example, a significant factor in type 2 diabetes
in humans but not rodents seems to be islet amyloid polypeptide, which contributes to beta cell
endoplasmic reticulum (ER) stress and B-cell loss through apoptosis58.
We now have at least a partial understanding of how molecular links between obesity, insulin
resistance, dyslipidaemias and hypertension contribute to diabetes, atherosclerosis, heart
failure and stroke (BOX 1). But the complexity and heterogeneity of MetSyn have made
mechanistic studies in humans especially difficult, and most molecular details have come from
studies with experimental models, particularly rodents3,7,55–57. A large number of MetSyn
models, including naturally occurring, and dietarily and genetically induced, have been
developed in rats and in mice55,56. Now the mouse is more widely used owing to its more
advanced genetics and its ease of genetic manipulation.
Inbred strains of rodents provide a uniform genetic background, so the effects of genetic or
environmental perturbations can be inferred. Also, the effects of genetic modifiers can be
examined on the background of disease-causing alleles. Tools such as tissue-specific knockouts
have enabled examination of the contributions of individual tissues or cell types to MetSyn.
For example, although adipose tissue, pancreas and muscle have long been known to contribute
to MetSyn, studies in rodents have indicated that several additional tissues, including brain,
liver, intestine, kidney and haematopoietic cells, can have causal roles. Although genes and
metabolic pathways are highly conserved between rodents and humans, and rodent models are
particularly valuable for dissecting mechanistic interactions, human studies remain essential
for determining their relevance to disease. For example, a significant factor in type 2 diabetes
in humans but not rodents seems to be islet amyloid polypeptide, which contributes to beta cell
endoplasmic reticulum (ER) stress and B-cell loss through apoptosis58.
Fuel partitioning
Insulin resistance is a central feature of MetSyn, and mouse models have had a major impact
on our understanding of how dysregulated lipid and glucose metabolism contribute to its
development. In 1963, Randle proposed a glucose–fatty acid cycle in an attempt to explain the
inhibition of glucose oxidation by excess fatty acids59, and it is now known that in addition to
their effect on glucose oxidation, fatty acids reduce insulin-stimulated glucose uptake into
muscle60. Although Randle’s model did not address insulin signalling, it made several
predictions that have since been tested in mouse models. For example, the fatty acid effect on
insulin sensitivity is abrogated by deletion of fatty acid transport protein 1 (REF. 61), indicating
that fatty-acid metabolites contribute to insulin resistance.
The question of why fatty acid uptake is linked to insulin resistance has been explored through
genetic manipulation of enzymes that incorporate fatty acids into specific lipid species in mouse
models. An initial hypothesis was that the incorporation of excess fatty acids into
triacylglycerol (that is, fat) in tissues such as liver, skeletal muscle and pancreatic beta cells
might cause insulin resistance. Manipulation of enzymes that convert fatty acids into various
glycerolipids and eventually into triacylglycerol demonstrated that diacylglycerol, the
immediate precursor of triacylglycerol and a lipid signalling molecule, is the culprit associated
with reduced insulin sensitivity62. In addition, a structurally related lipid, ceramide, also
modulates insulin sensitivity through the insulin signalling pathway63.
Insulin resistance is a central feature of MetSyn, and mouse models have had a major impact
on our understanding of how dysregulated lipid and glucose metabolism contribute to its
development. In 1963, Randle proposed a glucose–fatty acid cycle in an attempt to explain the
inhibition of glucose oxidation by excess fatty acids59, and it is now known that in addition to
their effect on glucose oxidation, fatty acids reduce insulin-stimulated glucose uptake into
muscle60. Although Randle’s model did not address insulin signalling, it made several
predictions that have since been tested in mouse models. For example, the fatty acid effect on
insulin sensitivity is abrogated by deletion of fatty acid transport protein 1 (REF. 61), indicating
that fatty-acid metabolites contribute to insulin resistance.
The question of why fatty acid uptake is linked to insulin resistance has been explored through
genetic manipulation of enzymes that incorporate fatty acids into specific lipid species in mouse
models. An initial hypothesis was that the incorporation of excess fatty acids into
triacylglycerol (that is, fat) in tissues such as liver, skeletal muscle and pancreatic beta cells
might cause insulin resistance. Manipulation of enzymes that convert fatty acids into various
glycerolipids and eventually into triacylglycerol demonstrated that diacylglycerol, the
immediate precursor of triacylglycerol and a lipid signalling molecule, is the culprit associated
with reduced insulin sensitivity62. In addition, a structurally related lipid, ceramide, also
modulates insulin sensitivity through the insulin signalling pathway63.
Mitochondria
Type 2 diabetes is associated with a decline in oxidative phosphorylation and commensurately
diminished aerobic capacity64. A similar decline in oxidative capacity seen in elderly
subjects65 is possibly linked to the sharp increase in diabetes that occurs with age. As
mitochondria are essential for oxidative metabolism, attention has focused on impaired
mitochondrial function in muscle and other tissues as a contributor to insulin resistance.
Microarray analysis in muscle tissue of insulin resistant and diabetic subjects revealed a global,
albeit modest, downregulation of genes encoding a subset of enzymes of oxidative
phosphorylation and mitochondrial function66. In addition, insulin resistant non-diabetic
offspring of patients with type 2 diabetes show a similar trait, implying that it is heritable and
potentially causal for diabetes67.
The transcriptional co-activator peroxisome proliferator-activated receptor gamma,
coactivator 1 alpha (PRGC1; also known as PGC1α) has a role in mitochondriogenesis and the
regulation of oxidative phosphorylation genes. However, testing of PRGC1 in patient groups
revealed that its expression levels are not necessarily altered in diabetes68, and a mouse model
with muscle-specific deletion of PRGC1 had increased insulin sensitivity rather than the
predicted insulin resistance69. These observations suggested that it is not reduced
mitochondrial fatty acid oxidation rate, but inefficient oxidation in conjunction with lipid
overload leading to an accumulation of oxidative intermediates, that is associated with insulin
resistance70. In support of this hypothesis, mice deficient for malonyl-CoA decarboxylase, an
enzyme that metabolizes an inhibitor of fatty acid uptake into the mitochondria, failed to
accumulate fatty acid oxidation intermediates and were rescued from insulin resistance induced
by a high-fat diet70.
Type 2 diabetes is associated with a decline in oxidative phosphorylation and commensurately
diminished aerobic capacity64. A similar decline in oxidative capacity seen in elderly
subjects65 is possibly linked to the sharp increase in diabetes that occurs with age. As
mitochondria are essential for oxidative metabolism, attention has focused on impaired
mitochondrial function in muscle and other tissues as a contributor to insulin resistance.
Microarray analysis in muscle tissue of insulin resistant and diabetic subjects revealed a global,
albeit modest, downregulation of genes encoding a subset of enzymes of oxidative
phosphorylation and mitochondrial function66. In addition, insulin resistant non-diabetic
offspring of patients with type 2 diabetes show a similar trait, implying that it is heritable and
potentially causal for diabetes67.
The transcriptional co-activator peroxisome proliferator-activated receptor gamma,
coactivator 1 alpha (PRGC1; also known as PGC1α) has a role in mitochondriogenesis and the
regulation of oxidative phosphorylation genes. However, testing of PRGC1 in patient groups
revealed that its expression levels are not necessarily altered in diabetes68, and a mouse model
with muscle-specific deletion of PRGC1 had increased insulin sensitivity rather than the
predicted insulin resistance69. These observations suggested that it is not reduced
mitochondrial fatty acid oxidation rate, but inefficient oxidation in conjunction with lipid
overload leading to an accumulation of oxidative intermediates, that is associated with insulin
resistance70. In support of this hypothesis, mice deficient for malonyl-CoA decarboxylase, an
enzyme that metabolizes an inhibitor of fatty acid uptake into the mitochondria, failed to
accumulate fatty acid oxidation intermediates and were rescued from insulin resistance induced
by a high-fat diet70.
Inflammation
MetSyn and its component traits, such as obesity and insulin resistance, have been associated
with chronic inflammation, as evidenced by elevations in circulating levels of Creactive protein
(CRP), tumour necrosis factor α (TNFα), fibrinogen, platelet activator inhibitor 1 and
interleukin-6 (IL6)71,72. Although it was initially suspected that the liver contributed most of
these cytokines, microarray studies of adipose tissue from lean versus obese mice revealed that
adipose tissue from obese mice contains a substantial macrophage population, which correlates
with adiposity and is responsible for the production of many of these cytokines73,74. Dietary
cholesterol enhances the accumulation of adipose tissue macrophages in LDL-receptordeficient
mice, providing an additional link between obesity, insulin resistance and
atherosclerosis — key components of MetSyn75.
Mouse models have shed light on factors that contribute to the recruitment of macrophages to
adipose tissue in obesity. The monocyte chemoattractant proteins (MCPs), which are required
for recruitment of monocytes to sites of injury, seem to play a part, as mice with genetic
deficiency of MCP1 or its receptor CCR2 exhibit reduced macrophage accumulation in
obesity76,77. Macrophages that are present in obese adipose tissue differ from resident
macrophages. Resident tissue macrophages typically exist in a quiescent state (the alternative
activation state) and do not actively secrete immune modulatory factors. However,
macrophages in obese adipose tissue resemble those that are stimulated by foreign pathogens
in conjunction with T lymphocytes, which become activated to secrete inflammatory cytokines
(the classically activated state)78. The nuclear receptor peroxisome proliferator-activated
receptor gamma (PPARG) might help regulate this process, as macrophage-specific disruption
of the PPARG gene impairs alternative macrophage activation and promotes the development
of diet-induced obesity and insulin resistance. Understanding the alterations that elicit
inflammation in adipose tissue may provide promising new therapeutic targets.
MetSyn and its component traits, such as obesity and insulin resistance, have been associated
with chronic inflammation, as evidenced by elevations in circulating levels of Creactive protein
(CRP), tumour necrosis factor α (TNFα), fibrinogen, platelet activator inhibitor 1 and
interleukin-6 (IL6)71,72. Although it was initially suspected that the liver contributed most of
these cytokines, microarray studies of adipose tissue from lean versus obese mice revealed that
adipose tissue from obese mice contains a substantial macrophage population, which correlates
with adiposity and is responsible for the production of many of these cytokines73,74. Dietary
cholesterol enhances the accumulation of adipose tissue macrophages in LDL-receptordeficient
mice, providing an additional link between obesity, insulin resistance and
atherosclerosis — key components of MetSyn75.
Mouse models have shed light on factors that contribute to the recruitment of macrophages to
adipose tissue in obesity. The monocyte chemoattractant proteins (MCPs), which are required
for recruitment of monocytes to sites of injury, seem to play a part, as mice with genetic
deficiency of MCP1 or its receptor CCR2 exhibit reduced macrophage accumulation in
obesity76,77. Macrophages that are present in obese adipose tissue differ from resident
macrophages. Resident tissue macrophages typically exist in a quiescent state (the alternative
activation state) and do not actively secrete immune modulatory factors. However,
macrophages in obese adipose tissue resemble those that are stimulated by foreign pathogens
in conjunction with T lymphocytes, which become activated to secrete inflammatory cytokines
(the classically activated state)78. The nuclear receptor peroxisome proliferator-activated
receptor gamma (PPARG) might help regulate this process, as macrophage-specific disruption
of the PPARG gene impairs alternative macrophage activation and promotes the development
of diet-induced obesity and insulin resistance. Understanding the alterations that elicit
inflammation in adipose tissue may provide promising new therapeutic targets.
Endoplasmic reticulum stress
Insulin resistance is also associated with oxidative stress that affects the ER in adipose and
other tissues79. As the site of protein and lipid synthesis, the ER has a central metabolic role
in integrating nutrient signals. Nutrient overload increases demand for adipose tissue
expansion, requiring increased lipid and protein synthesis. This provokes an adaptive response
in the ER known as the unfolded protein response (UPR), which activates several signalling
pathways to re-establish homeostasis by limiting protein synthesis and inducing transcription
of chaperone proteins that assist with the unfolded protein load80. As with the induction of
macrophage infiltration and inflammation of adipose tissue described above, this adaptive
response might have negative consequences when it is present chronically in conditions such
as obesity, insulin resistance and atherosclerosis.
The UPR is activated in adipose tissue in mice by both dietarily and genetically induced
obesity81. Consequences of the UPR include activation of the Jun N-terminal kinase (JNK)
pathway with subsequent serine phosphorylation of insulin receptor substrate 1 (IRS1) and
insulin resistance. Furthermore, haploinsufficiency for X-box binding protein 1 (XBP1), a
transcription factor for chaperone proteins and ER biogenesis proteins, leads to increased JNK
activation and insulin resistance. The UPR also activates nuclear factor-κB (NF-κB), a key
inflammatory transcription factor, leading to increased expression of TNFα, IL6 and MCP1
and impaired insulin sensitivity82. The UPR is also induced in macrophages and endothelial
cells83,84, indicating its key role in several key tissues in MetSyn.
Identification of ER stress and the UPR as inflammatory mechanisms in obesity, insulin
resistance and atherosclerosis has also suggested potential therapeutic targets. For example,
treatment of genetically obese mice with the chemical chaperones phenylbutyric acid and
taurine-conjugated ursodeoxycholic acid decreases UPR signalling in adipose tissue and liver
and improves systemic insulin sensitivity without affecting body weight85. Similarly,
administration of salicylic acid, which inhibits the inhibitor of NF-κB kinase β (IKKβ)–NF-
κB pathway, improves insulin signalling and reverses hyperglycaemia, hyperinsulinaemia and
hyperlipidaemia in obese rodents86, and prevents free fatty acid-induced hepatic insulin
resistance87.
Insulin resistance is also associated with oxidative stress that affects the ER in adipose and
other tissues79. As the site of protein and lipid synthesis, the ER has a central metabolic role
in integrating nutrient signals. Nutrient overload increases demand for adipose tissue
expansion, requiring increased lipid and protein synthesis. This provokes an adaptive response
in the ER known as the unfolded protein response (UPR), which activates several signalling
pathways to re-establish homeostasis by limiting protein synthesis and inducing transcription
of chaperone proteins that assist with the unfolded protein load80. As with the induction of
macrophage infiltration and inflammation of adipose tissue described above, this adaptive
response might have negative consequences when it is present chronically in conditions such
as obesity, insulin resistance and atherosclerosis.
The UPR is activated in adipose tissue in mice by both dietarily and genetically induced
obesity81. Consequences of the UPR include activation of the Jun N-terminal kinase (JNK)
pathway with subsequent serine phosphorylation of insulin receptor substrate 1 (IRS1) and
insulin resistance. Furthermore, haploinsufficiency for X-box binding protein 1 (XBP1), a
transcription factor for chaperone proteins and ER biogenesis proteins, leads to increased JNK
activation and insulin resistance. The UPR also activates nuclear factor-κB (NF-κB), a key
inflammatory transcription factor, leading to increased expression of TNFα, IL6 and MCP1
and impaired insulin sensitivity82. The UPR is also induced in macrophages and endothelial
cells83,84, indicating its key role in several key tissues in MetSyn.
Identification of ER stress and the UPR as inflammatory mechanisms in obesity, insulin
resistance and atherosclerosis has also suggested potential therapeutic targets. For example,
treatment of genetically obese mice with the chemical chaperones phenylbutyric acid and
taurine-conjugated ursodeoxycholic acid decreases UPR signalling in adipose tissue and liver
and improves systemic insulin sensitivity without affecting body weight85. Similarly,
administration of salicylic acid, which inhibits the inhibitor of NF-κB kinase β (IKKβ)–NF-
κB pathway, improves insulin signalling and reverses hyperglycaemia, hyperinsulinaemia and
hyperlipidaemia in obese rodents86, and prevents free fatty acid-induced hepatic insulin
resistance87.
Diabetes and cardiovascular disease
The causal relationships between MetSyn and diabetes have been extensively studied3–7.
Although many obese individuals develop insulin resistance, only a fraction go on to develop
diabetes. Obese rodent models, such as leptin-deficient mice or rats, all develop insulin
resistance, but whether they develop diabetes is dependent upon the genetic background3,55.
The beta cells of resistant strains proliferate to keep up with the increasing insulin demand,
whereas the beta cells of susceptible strains undergo apoptosis. Several mechanisms have been
proposed for increased beta-cell apoptosis, including oxygen free radicals, free fatty acid
toxicity, interleukin-1β and formation of islet amyloid polypeptide toxic oligomers58.
Positional cloning of a QTL in mice identified a novel gene, VPS10 domain receptor protein
SORCS 1 (Sorcs1), that might be involved in the maintenance of islet vasculature and islet
growth88. As suggested by recent GWA studies as well as studies with animal models, diabetes
development is determined primarily by pancreatic beta-cell responses to insulin resistance.
MetSyn is also a strong susceptibility factor for CVD. A number of genetically engineered
mouse models that develop hypercholesterolaemia and relatively advanced atherosclerotic
lesions are now used for atherosclerosis research. The most widely used are apolipoprotein E
null and LDL receptor null mice. The latter, when on the genetic background of strain C57BL/
6J and fed a high-fat diet, develops all the MetSyn traits with the exception of
hypertension89. Although these mice develop atherosclerotic lesions, they lack some features
that are crucial in human disease, such as the rupture or erosion of lesions, which triggers
thrombus formation and is the most common cause of myocardial infarction in humans90.
Atherosclerosis is a disease of the large arteries that is characterized by an accumulation of
necrotic cell debris, cholesterol, fibrous tissue and inflammatory cells in the subendothelial
space. The effects of obesity and insulin resistance on traditional risk factors such as HDL and
triglyceride levels and blood pressure are important. Recent data have also revealed common
variations in the anti-inflammatory properties of HDL91. There is emerging evidence that
visceral fat secretes proinflammatory and prothrombotic factors, such as leptin, adiponectin,
IL6, TNFα and plasminogen activator inhibitor, type I (PAI1), that exert direct effects on the
vessel wall7. The interactions between diabetes and CVD have proven difficult to address
owing to a paucity of animal models92. However, streptozotocin-induced elevations of glucose
do accelerate atherosclerosis on a hyperlipidaemic background, mediated in part by the receptor
for advanced glycation products93.
Obesity has been associated with structural and functional changes of the heart94. Interestingly,
obese patients tend to have a better chance of survival once they are diagnosed with CVD, a
phenomenon termed the ‘obesity paradox’95. Obesity in animal models is associated with
coincident morbidities, including cardiac hypertrophy, cardiac adiposity and valvular
dysfunction94. Obesity increases the use of fatty acids and decreases the use of glucose as
myocardial substrates96, and recent studies have revealed that these changes are accompanied
by increased myocardial oxygen consumption and decreased cardiac efficiency97.
Mitochondrial numbers increase, in part due to PRGC1 and PPARA upregulation, but there is
reduced oxygen consumption and ATP generation and increased superoxide production.
Excess accumulation of lipids, such as ceramide, in cardiomyocytes can directly induce
cardiomyopathy, a deterioration of the myocardium98.
The causal relationships between MetSyn and diabetes have been extensively studied3–7.
Although many obese individuals develop insulin resistance, only a fraction go on to develop
diabetes. Obese rodent models, such as leptin-deficient mice or rats, all develop insulin
resistance, but whether they develop diabetes is dependent upon the genetic background3,55.
The beta cells of resistant strains proliferate to keep up with the increasing insulin demand,
whereas the beta cells of susceptible strains undergo apoptosis. Several mechanisms have been
proposed for increased beta-cell apoptosis, including oxygen free radicals, free fatty acid
toxicity, interleukin-1β and formation of islet amyloid polypeptide toxic oligomers58.
Positional cloning of a QTL in mice identified a novel gene, VPS10 domain receptor protein
SORCS 1 (Sorcs1), that might be involved in the maintenance of islet vasculature and islet
growth88. As suggested by recent GWA studies as well as studies with animal models, diabetes
development is determined primarily by pancreatic beta-cell responses to insulin resistance.
MetSyn is also a strong susceptibility factor for CVD. A number of genetically engineered
mouse models that develop hypercholesterolaemia and relatively advanced atherosclerotic
lesions are now used for atherosclerosis research. The most widely used are apolipoprotein E
null and LDL receptor null mice. The latter, when on the genetic background of strain C57BL/
6J and fed a high-fat diet, develops all the MetSyn traits with the exception of
hypertension89. Although these mice develop atherosclerotic lesions, they lack some features
that are crucial in human disease, such as the rupture or erosion of lesions, which triggers
thrombus formation and is the most common cause of myocardial infarction in humans90.
Atherosclerosis is a disease of the large arteries that is characterized by an accumulation of
necrotic cell debris, cholesterol, fibrous tissue and inflammatory cells in the subendothelial
space. The effects of obesity and insulin resistance on traditional risk factors such as HDL and
triglyceride levels and blood pressure are important. Recent data have also revealed common
variations in the anti-inflammatory properties of HDL91. There is emerging evidence that
visceral fat secretes proinflammatory and prothrombotic factors, such as leptin, adiponectin,
IL6, TNFα and plasminogen activator inhibitor, type I (PAI1), that exert direct effects on the
vessel wall7. The interactions between diabetes and CVD have proven difficult to address
owing to a paucity of animal models92. However, streptozotocin-induced elevations of glucose
do accelerate atherosclerosis on a hyperlipidaemic background, mediated in part by the receptor
for advanced glycation products93.
Obesity has been associated with structural and functional changes of the heart94. Interestingly,
obese patients tend to have a better chance of survival once they are diagnosed with CVD, a
phenomenon termed the ‘obesity paradox’95. Obesity in animal models is associated with
coincident morbidities, including cardiac hypertrophy, cardiac adiposity and valvular
dysfunction94. Obesity increases the use of fatty acids and decreases the use of glucose as
myocardial substrates96, and recent studies have revealed that these changes are accompanied
by increased myocardial oxygen consumption and decreased cardiac efficiency97.
Mitochondrial numbers increase, in part due to PRGC1 and PPARA upregulation, but there is
reduced oxygen consumption and ATP generation and increased superoxide production.
Excess accumulation of lipids, such as ceramide, in cardiomyocytes can directly induce
cardiomyopathy, a deterioration of the myocardium98.
Addressing the complexity of MetSyn
Only a small fraction of the genetic component of MetSyn is explained by known variations,
and mechanistic insights have been based largely on qualitative analyses, such as studies of
knockout mice, without regard to epistasis or other interactions. Indeed, it seems unlikely that
the highly complex gene–gene and gene–environment interactions that are central to MetSyn
can be easily modelled in transgenic mice. Recent studies imply that systems-based approaches
might be able to better address such complex interactions.
Only a small fraction of the genetic component of MetSyn is explained by known variations,
and mechanistic insights have been based largely on qualitative analyses, such as studies of
knockout mice, without regard to epistasis or other interactions. Indeed, it seems unlikely that
the highly complex gene–gene and gene–environment interactions that are central to MetSyn
can be easily modelled in transgenic mice. Recent studies imply that systems-based approaches
might be able to better address such complex interactions.
Systems-based approaches
Systems biology uses technologies such as gene expression microarrays and mass spectrometry
in combination with computational and statistical tools to address complex systems. MetSyn
involves inputs from hundreds of genes, many environmental factors and a host of tissues.
Therefore, analysing the individual components of a system is not sufficient, as it is important
to know how these components interact with each other and how these interactions differ in
disease states. Genetic and environmental factors influence clinical traits by perturbing
molecular networks, and systems-based approaches have the potential to interrogate these
molecular phenotypes and identify patterns associated with disease.
Currently, expression arrays provide the only quantitative, genome-wide window into
molecular phenotypes, but high-throughput technologies for screening of proteins and
metabolites, such as mass spectrometry, are also quite advanced. Microarray studies have
revealed small alterations in expression levels of many genes, thus highlighting important
pathways in MetSyn. These include altered macrophage-derived inflammatory gene expression
in adipose tissue in obesity73 and, although not reproduced in other populations, altered
oxidative phosphorylation gene expression in diabetic muscle66,99. Both of these findings
spurred extensive investigation and have elucidated new aspects of the pathology of MetSyn.
Systems-based approaches seek to move beyond simple correlations of levels to determine
how components interact. Such interactions can be based on known connections from the
literature, experimental determination of physical interactions or experimental perturbations
to test for co-regulation. Frequently, these interactions are described in terms of networks that
consist of parts (nodes) and their connections (edges) (FIG. 2). The dynamics of the system
can be mathematically modelled, allowing prediction of the response of the system to genetic
or environmental perturbations100–102
The first biological networks were based on the large body of knowledge of metabolic pathways
gained from over half of a century of biochemical studies103. Subsequent studies have extended
such bibliomic data and combined it with genome annotation104. They have shown that
biological circuitry is not random and that it tends to obey certain principles. The network
concept has proven extremely useful in studies of metabolic traits, revealing ‘emergent
properties’ not otherwise apparent as well as novel drug targets102–104.
Systems biology uses technologies such as gene expression microarrays and mass spectrometry
in combination with computational and statistical tools to address complex systems. MetSyn
involves inputs from hundreds of genes, many environmental factors and a host of tissues.
Therefore, analysing the individual components of a system is not sufficient, as it is important
to know how these components interact with each other and how these interactions differ in
disease states. Genetic and environmental factors influence clinical traits by perturbing
molecular networks, and systems-based approaches have the potential to interrogate these
molecular phenotypes and identify patterns associated with disease.
Currently, expression arrays provide the only quantitative, genome-wide window into
molecular phenotypes, but high-throughput technologies for screening of proteins and
metabolites, such as mass spectrometry, are also quite advanced. Microarray studies have
revealed small alterations in expression levels of many genes, thus highlighting important
pathways in MetSyn. These include altered macrophage-derived inflammatory gene expression
in adipose tissue in obesity73 and, although not reproduced in other populations, altered
oxidative phosphorylation gene expression in diabetic muscle66,99. Both of these findings
spurred extensive investigation and have elucidated new aspects of the pathology of MetSyn.
Systems-based approaches seek to move beyond simple correlations of levels to determine
how components interact. Such interactions can be based on known connections from the
literature, experimental determination of physical interactions or experimental perturbations
to test for co-regulation. Frequently, these interactions are described in terms of networks that
consist of parts (nodes) and their connections (edges) (FIG. 2). The dynamics of the system
can be mathematically modelled, allowing prediction of the response of the system to genetic
or environmental perturbations100–102
The first biological networks were based on the large body of knowledge of metabolic pathways
gained from over half of a century of biochemical studies103. Subsequent studies have extended
such bibliomic data and combined it with genome annotation104. They have shown that
biological circuitry is not random and that it tends to obey certain principles. The network
concept has proven extremely useful in studies of metabolic traits, revealing ‘emergent
properties’ not otherwise apparent as well as novel drug targets102–104.
Integrative genetics
One systems-based approach that has proven particularly powerful for analysis of MetSyn
involves the integration of common DNA variation, global expression array analysis and
clinical phenotypes (FIG. 2). In any natural population there will be thousands of
polymorphisms that perturb gene expression. If transcript levels are quantified in genetically
randomized individuals, such as patients with MetSyn or mice generated from a cross between
strains differing in MetSyn traits, then these levels can be related to both DNA variation and
to clinical traits by genetic mapping and correlation105,106.
Like other quantitative traits, transcript levels can be mapped using linkage or association. The
resulting loci are termed expression QTLs (eQTLs) or expression SnPs (eSnPs), respectively.
Using microarray technologies it is feasible to identify eQTLs for thousands of genes in genetic
crosses in rodents, or in family or population studies in humans106–108. Databases of eQTLs
are proving useful for prioritizing candidate genes for genetic traits. A good example of the
use of eQTLs to help identify genes underlying MetSyn is the identification of osteoglycin
(Ogn) in the control of left ventricular mass (LVM)109. Rat recombinant inbred strains
segregating for complex variations of LVM were studied using eQTL analysis of the heart,
and a number of cis-acting eQTLs localizing with a LVM QTL were identified. These were
prioritized on the basis of the strength of the eQTL and correlation with LVM in both rats and
human heart biopsies, and studies with Ogn knockout mice confirmed the identity of the
gene109. Other findings include links between Cd36 and pressure elevation in rats110, and
between Abcc6 and vascular calcification in mice111.
The fact that genetically randomized populations exhibit multiple perturbations influencing
both molecular and clinical traits offers an opportunity to model causal interactions (BOX 4).
In such modelling, the direction of the interactions between DNA and the traits can be inferred
on the basis of conditional probabilities. This approach was recently applied to identify
candidate genes involved in obesity and other MetSyn traits in a segregating mouse population,
and several genes were experimentally validated using transgenic approaches107,112.
One systems-based approach that has proven particularly powerful for analysis of MetSyn
involves the integration of common DNA variation, global expression array analysis and
clinical phenotypes (FIG. 2). In any natural population there will be thousands of
polymorphisms that perturb gene expression. If transcript levels are quantified in genetically
randomized individuals, such as patients with MetSyn or mice generated from a cross between
strains differing in MetSyn traits, then these levels can be related to both DNA variation and
to clinical traits by genetic mapping and correlation105,106.
Like other quantitative traits, transcript levels can be mapped using linkage or association. The
resulting loci are termed expression QTLs (eQTLs) or expression SnPs (eSnPs), respectively.
Using microarray technologies it is feasible to identify eQTLs for thousands of genes in genetic
crosses in rodents, or in family or population studies in humans106–108. Databases of eQTLs
are proving useful for prioritizing candidate genes for genetic traits. A good example of the
use of eQTLs to help identify genes underlying MetSyn is the identification of osteoglycin
(Ogn) in the control of left ventricular mass (LVM)109. Rat recombinant inbred strains
segregating for complex variations of LVM were studied using eQTL analysis of the heart,
and a number of cis-acting eQTLs localizing with a LVM QTL were identified. These were
prioritized on the basis of the strength of the eQTL and correlation with LVM in both rats and
human heart biopsies, and studies with Ogn knockout mice confirmed the identity of the
gene109. Other findings include links between Cd36 and pressure elevation in rats110, and
between Abcc6 and vascular calcification in mice111.
The fact that genetically randomized populations exhibit multiple perturbations influencing
both molecular and clinical traits offers an opportunity to model causal interactions (BOX 4).
In such modelling, the direction of the interactions between DNA and the traits can be inferred
on the basis of conditional probabilities. This approach was recently applied to identify
candidate genes involved in obesity and other MetSyn traits in a segregating mouse population,
and several genes were experimentally validated using transgenic approaches107,112.
Box 4
Causal modelling of complex traits
Integrative genetics can be used to model causal interactions between DNA variation and
transcript levels, as well as clinical traits in genetically randomized populations. An
important aspect of this approach is the complex nature of the genetic perturbations.
Whereas single gene perturbations, such as transgenic mice, allow causality to be
established, they have limited power to resolve the pleiotropic and homeostatic interactions
resulting from the perturbation. Conversely, multiple perturbations, such as populations
segregating for common genetic variations, allow detailed analyses of the interactions131.
The concept is illustrated in the figure. Panel a shows analysis of elements A and B that are
perturbed by a single gene, G1. The relationship between A and B (whether G1 acts on A
which then acts on B or vice versa, or whether G1 perturbs the two independently) cannot
be determined. However, the introduction of a second perturbing gene, G2, shown in panel
b, can clarify the relationship. For example, suppose that G2 acts solely on B, and G1 acts
solely on A, which in turn perturbs B. In that case, the correlation between G1 and B would
be expected to be the product of the correlation between G1 and A and the correlation
between A and B. In the context of integrative genetics (FIG. 2), such relationships can be
modelled using conditional probabilities, with the DNA variation serving as a causal anchor.
For example, as illustrated in panel c, there are three likely relationships between DNA
variation, the expression of a gene (transcript) and a clinical trait132. The likelihood of each
model can be calculated and the one with the best fit chosen. Several algorithms for such
causality modelling have been reported (for example, REF. 112), and recent studies with
transgenic mice have validated the approach for the identification of genes for adiposity
and insulin resistance107,112. More general methodologies, such as constrained Bayesian
networks, can be used to model causal interactions among the elements of a biological
network, as shown in FIG. 2d.
Data from such studies can be used to construct co-expression networks in which the nodes
are transcript levels and the edges represent correlations between transcripts (FIG. 2b). Such
modelling is based on the assumption that genes with correlated expression are likely to be
functionally associated (although other explanations, such as linkage or linkage disequilibrium,
or the impact of the clinical trait itself, could also result in correlations). It is also clear that
many functionally associated genes would not be correlated, given that much regulation is posttranscriptional.
Thus, such networks are clearly approximations of the underlying biology, and
integration with other data sets and approaches is important. Nevertheless, groups of genes, or
‘modules’, identified by co-expression modelling are significantly enriched for functionally
related genes. These modules have proven useful for annotating novel genes and revealing
regulatory mechanisms (for example, REF. 113). Interestingly, certain modules correlate
strongly with clinical traits (FIG. 2c). In some cases, the modules explain a much greater
fraction of the variance of the clinical trait than any individual QTL, suggesting that they reflect
a higher order network that integrates multiple genetic inputs. Co-expression networks can also
be integrated with causal modelling to construct ‘directed’ networks (FIG. 2d). This approach
was recently used to identify key genetic drivers in a macrophage-enriched subnetwork that is
strongly associated with a number of MetSyn traits107.
Although it is difficult to obtain appropriate human tissues, recent systems-based analyses of
liver and adipose tissue biopsies clearly show that these network modelling approaches are
applicable to complex traits in humans30,114. Importantly, there is evidence that co-expression
networks exhibit a degree of conservation between mice and humans107. Recently, such
modelling has helped identify the likely susceptibility genes at several loci identified in human
GWA studies30. Such integrative genetics approaches are now being expanded to include
proteomic and metabolomic data. For example, analysis of levels of 67 metabolites in a cross
between two strains of mice was used to construct a causal network linking gene expression
and metabolic changes. The network was validated by examining responses to metabolic
perturbations115.
Causal modelling of complex traits
Integrative genetics can be used to model causal interactions between DNA variation and
transcript levels, as well as clinical traits in genetically randomized populations. An
important aspect of this approach is the complex nature of the genetic perturbations.
Whereas single gene perturbations, such as transgenic mice, allow causality to be
established, they have limited power to resolve the pleiotropic and homeostatic interactions
resulting from the perturbation. Conversely, multiple perturbations, such as populations
segregating for common genetic variations, allow detailed analyses of the interactions131.
The concept is illustrated in the figure. Panel a shows analysis of elements A and B that are
perturbed by a single gene, G1. The relationship between A and B (whether G1 acts on A
which then acts on B or vice versa, or whether G1 perturbs the two independently) cannot
be determined. However, the introduction of a second perturbing gene, G2, shown in panel
b, can clarify the relationship. For example, suppose that G2 acts solely on B, and G1 acts
solely on A, which in turn perturbs B. In that case, the correlation between G1 and B would
be expected to be the product of the correlation between G1 and A and the correlation
between A and B. In the context of integrative genetics (FIG. 2), such relationships can be
modelled using conditional probabilities, with the DNA variation serving as a causal anchor.
For example, as illustrated in panel c, there are three likely relationships between DNA
variation, the expression of a gene (transcript) and a clinical trait132. The likelihood of each
model can be calculated and the one with the best fit chosen. Several algorithms for such
causality modelling have been reported (for example, REF. 112), and recent studies with
transgenic mice have validated the approach for the identification of genes for adiposity
and insulin resistance107,112. More general methodologies, such as constrained Bayesian
networks, can be used to model causal interactions among the elements of a biological
network, as shown in FIG. 2d.
Data from such studies can be used to construct co-expression networks in which the nodes
are transcript levels and the edges represent correlations between transcripts (FIG. 2b). Such
modelling is based on the assumption that genes with correlated expression are likely to be
functionally associated (although other explanations, such as linkage or linkage disequilibrium,
or the impact of the clinical trait itself, could also result in correlations). It is also clear that
many functionally associated genes would not be correlated, given that much regulation is posttranscriptional.
Thus, such networks are clearly approximations of the underlying biology, and
integration with other data sets and approaches is important. Nevertheless, groups of genes, or
‘modules’, identified by co-expression modelling are significantly enriched for functionally
related genes. These modules have proven useful for annotating novel genes and revealing
regulatory mechanisms (for example, REF. 113). Interestingly, certain modules correlate
strongly with clinical traits (FIG. 2c). In some cases, the modules explain a much greater
fraction of the variance of the clinical trait than any individual QTL, suggesting that they reflect
a higher order network that integrates multiple genetic inputs. Co-expression networks can also
be integrated with causal modelling to construct ‘directed’ networks (FIG. 2d). This approach
was recently used to identify key genetic drivers in a macrophage-enriched subnetwork that is
strongly associated with a number of MetSyn traits107.
Although it is difficult to obtain appropriate human tissues, recent systems-based analyses of
liver and adipose tissue biopsies clearly show that these network modelling approaches are
applicable to complex traits in humans30,114. Importantly, there is evidence that co-expression
networks exhibit a degree of conservation between mice and humans107. Recently, such
modelling has helped identify the likely susceptibility genes at several loci identified in human
GWA studies30. Such integrative genetics approaches are now being expanded to include
proteomic and metabolomic data. For example, analysis of levels of 67 metabolites in a cross
between two strains of mice was used to construct a causal network linking gene expression
and metabolic changes. The network was validated by examining responses to metabolic
perturbations115.
Future directions
Recent advances in MetSyn have been impressive, but there remain large areas of ignorance,
such as understanding details of the genetic and environmental interactions that are involved
in MetSyn. Many successes in identifying genes for MetSyn traits have occurred within the
last 2 years using GWA studies, and there are likely to be many additional findings in the next
several years. These might help reveal the nature of the ‘dark matter’ (BOX 3) — that is,
whether it results from complex interaction or simply from many variations, rare or common.
These are of course important issues for the development of personalized medicine.
We believe that considerable effort now should be devoted to examining MetSyn from a broad
perspective rather than focusing narrowly on individual pathways or metabolic components.
This will require the application of interdisciplinary approaches, such as genetics, genomics,
proteomics, metabolomics, physiology and mathematical modelling. This should eventually
enable the development of a holistic picture of MetSyn, integrating information from multiple
scales, including genes, transcripts, proteins, organelles, cells, tissues and organisms.
Despite great advances in mechanistic understanding, recent efforts to develop new therapies
for MetSyn have a poor record of success. CVD has certainly not disappeared with the advent
of powerful cholesterol-lowering drugs, and the widely used existing drugs — most notably
the statins — are not without their side-effects. Thus, there are important unmet therapeutic
needs. The identification of the metabolic pathways that are perturbed in MetSyn, and of the
genetic networks that control them, might provide a clearer understanding of how the disease
develops and how the components interact. This might suggest, in turn, the most rational targets
for the development of effective and safe therapeutic strategies.
Recent advances in MetSyn have been impressive, but there remain large areas of ignorance,
such as understanding details of the genetic and environmental interactions that are involved
in MetSyn. Many successes in identifying genes for MetSyn traits have occurred within the
last 2 years using GWA studies, and there are likely to be many additional findings in the next
several years. These might help reveal the nature of the ‘dark matter’ (BOX 3) — that is,
whether it results from complex interaction or simply from many variations, rare or common.
These are of course important issues for the development of personalized medicine.
We believe that considerable effort now should be devoted to examining MetSyn from a broad
perspective rather than focusing narrowly on individual pathways or metabolic components.
This will require the application of interdisciplinary approaches, such as genetics, genomics,
proteomics, metabolomics, physiology and mathematical modelling. This should eventually
enable the development of a holistic picture of MetSyn, integrating information from multiple
scales, including genes, transcripts, proteins, organelles, cells, tissues and organisms.
Despite great advances in mechanistic understanding, recent efforts to develop new therapies
for MetSyn have a poor record of success. CVD has certainly not disappeared with the advent
of powerful cholesterol-lowering drugs, and the widely used existing drugs — most notably
the statins — are not without their side-effects. Thus, there are important unmet therapeutic
needs. The identification of the metabolic pathways that are perturbed in MetSyn, and of the
genetic networks that control them, might provide a clearer understanding of how the disease
develops and how the components interact. This might suggest, in turn, the most rational targets
for the development of effective and safe therapeutic strategies.
Acknowledgments
We thank our colleagues for valuable discussion, C. Farber and A. Ghazalpour for help with figures, and R. Chen for
secretarial assistance.
We thank our colleagues for valuable discussion, C. Farber and A. Ghazalpour for help with figures, and R. Chen for
secretarial assistance.
Glossary
Insulin resistance A condition in which normal amounts of insulin are inadequate to
produce a normal response from muscle, fat, liver or other cells. Such
insulin resistance can result in elevated glucose levels in the blood
owing to decreased uptake by cells, as well as effects on glycogen
storage and lipid metabolism
produce a normal response from muscle, fat, liver or other cells. Such
insulin resistance can result in elevated glucose levels in the blood
owing to decreased uptake by cells, as well as effects on glycogen
storage and lipid metabolism
Dyslipidaemia An abnormal or atypical pattern of lipoproteins in the blood.
Examples include low levels of high-density lipoprotein cholesterol
(hypoalphalipoproteinaemia), or elevated levels of triglyceride
(hypertriglyceridaemia) or cholesterol (hypercholesterolaemia)
Examples include low levels of high-density lipoprotein cholesterol
(hypoalphalipoproteinaemia), or elevated levels of triglyceride
(hypertriglyceridaemia) or cholesterol (hypercholesterolaemia)
Genome-wide
association study
(GWA study)
An examination of common genetic variation across the genome
designed to identify associations with traits such as common
diseases. Typically, several hundred thousand SNPs are interrogated
using microarray technologies
association study
(GWA study)
An examination of common genetic variation across the genome
designed to identify associations with traits such as common
diseases. Typically, several hundred thousand SNPs are interrogated
using microarray technologies
High-density
lipoprotein (HDL)
One of five classes of lipoproteins in the blood that transport
cholesterol and triglycerides between tissues. HDL levels are
inversely correlated with cardiovascular disease and thus are
hypothesized to be protective, perhaps by removing cholesterol from
atheroma
lipoprotein (HDL)
One of five classes of lipoproteins in the blood that transport
cholesterol and triglycerides between tissues. HDL levels are
inversely correlated with cardiovascular disease and thus are
hypothesized to be protective, perhaps by removing cholesterol from
atheroma
Heritability An estimate of the proportion of genetic variation in a population
that is attributable to genetic variation among individuals
that is attributable to genetic variation among individuals
Linkage analysis Analysis of the segregation patterns of alleles or loci in families or
experimental crosses. Such analysis is commonly used to map
genetic traits by testing whether a trait co-segregates with genetic
markers whose chromosomal locations are known
experimental crosses. Such analysis is commonly used to map
genetic traits by testing whether a trait co-segregates with genetic
markers whose chromosomal locations are known
Quantitative trait
locus (QTL)
A genetic locus that influences complex and usually continuous
traits, such as blood pressure or cholesterol levels. QTLs are
identified using linkage analysis
locus (QTL)
A genetic locus that influences complex and usually continuous
traits, such as blood pressure or cholesterol levels. QTLs are
identified using linkage analysis
Linkage
disequilibrium (LD)
In population genetics, LD is the nonrandom association of alleles.
For example, alleles of SNPs that reside near one another on a
chromosome often occur in nonrandom combinations owing to
infrequent recombination. LD is useful in genome-wide association
studies as it reduces the number of SNPs that must be interrogated
to determine genotypes across the genome. Conversely, strong LD
can complicate the identification of functional variants. LD should
not be confused with genetic linkage, which occurs when genetic
loci or alleles are inherited jointly, usually because they reside on
the same chromosome
disequilibrium (LD)
In population genetics, LD is the nonrandom association of alleles.
For example, alleles of SNPs that reside near one another on a
chromosome often occur in nonrandom combinations owing to
infrequent recombination. LD is useful in genome-wide association
studies as it reduces the number of SNPs that must be interrogated
to determine genotypes across the genome. Conversely, strong LD
can complicate the identification of functional variants. LD should
not be confused with genetic linkage, which occurs when genetic
loci or alleles are inherited jointly, usually because they reside on
the same chromosome
Visceral fat Fat that is located inside the peritoneal cavity, between internal
organs, as opposed to subcutaneous fat, which is found under the
skin, or intramuscular fat, which is interspersed in skeletal muscle
organs, as opposed to subcutaneous fat, which is found under the
skin, or intramuscular fat, which is interspersed in skeletal muscle
Correlation In statistics, a measure of the strength and direction of a linear
relationship between two variables. Usually measured as a
correlation coefficient
relationship between two variables. Usually measured as a
correlation coefficient
Epigenetics Changes in gene expression that are stable through cell division but
do not involve changes in the underlying DNA sequence. The most
common example is cellular differentiation, but it is clear that
environmental factors, such as maternal nutrition, can influence
epigenetic programming
do not involve changes in the underlying DNA sequence. The most
common example is cellular differentiation, but it is clear that
environmental factors, such as maternal nutrition, can influence
epigenetic programming
Ceramide A family of lipid molecules composed of sphingosine and a fatty
acid. In addition to being structural components of lipid bilayers, it
is now clear that ceramides can act as signalling molecules
acid. In addition to being structural components of lipid bilayers, it
is now clear that ceramides can act as signalling molecules
Oxidative
phosphorylation
A metabolic pathway that uses oxidation of nutrients to generate
ATP. The electron transport chain in mitochondria is the site of
oxidative phosphorylation in eukaryotes
phosphorylation
A metabolic pathway that uses oxidation of nutrients to generate
ATP. The electron transport chain in mitochondria is the site of
oxidative phosphorylation in eukaryotes
Haploinsufficiency A condition in a diploid organism in which a single functional copy
of a gene results in a phenotype, such as a disease. Genetically
randomized population, A population in which genotypes are
randomized owing to the random assortment of alleles during
gametogenesis
of a gene results in a phenotype, such as a disease. Genetically
randomized population, A population in which genotypes are
randomized owing to the random assortment of alleles during
gametogenesis
Conditional
probability
The probability of an event, A, given the occurrence of some other
event, B
probability
The probability of an event, A, given the occurrence of some other
event, B
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