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Genetic and epigenetic studies of adiposity and cardiometabolic disease

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Editorial summary Over 300 million adults are obese, but little is known about the impact of obesity on cardiovascular health. We discuss recent genetic and epigenetic studies of adiposity that indicate a causal role for general and central adiposity in cardiometabolic disease, and highlight potential mechanisms including insulin resistance and gene expression.
No caption available
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Relationships of general and central adiposity with cardiometabolic diseases and related traits identified through genetic and epigenetic studies. Two common phenotypes have been key to the genetic study of adiposity in humans: body mass index (BMI; in blue), which measures general adiposity; and waist-to-hip ratio adjusted for BMI (WHRadjBMI; in red), which captures central adiposity (that is, fat that collects around the central region of the body and may mark visceral fat deposits). Genome-wide association studies (GWASs) in BMI and WHRadjBMI have revealed 97 and 49 common variant loci, respectively, associated with the traits. While GWASs provide evidence for association between genetic variants and phenotypic outcomes, the variants implicated in these studies can be used in Mendelian randomization (MR) analyses to investigate causal relationships. MR studies using BMI-associated single nucleotide polymorphisms (SNPs) have established causal relationships of BMI on blood pressure, insulin resistance, DNA methylation (that is, alterations in gene expression), diabetes, and coronary heart disease. Similar studies, but for WHRadjBMI-associated SNPs, show similar causal relationships (excluding that for DNA methylation), and a causal role in stroke. The results indicate that not only general adiposity (indexed by BMI) but the distribution of adipose tissue in particular depots (indexed by WHRadjBMI) is crucial to the relationship between adiposity and cardiometabolic disease outcome
… 
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C O M M E N T Open Access
Genetic and epigenetic studies of adiposity
and cardiometabolic disease
Michael V. Holmes
1,2,3,4*
, Sara L. Pulit
5,6,7
and Cecilia M. Lindgren
4,5,6,8*
Editorial summary
Over 300 million adults are obese, but little is known
about the impact of obesity on cardiovascular health.
We discuss recent genetic and epigenetic studies of
adiposity that indicate a causal role for general and
central adiposity in cardiometabolic disease, and
highlight potential mechanisms including insulin
resistance and gene expression.
Background
Obesity, an excess of adiposity in which the body mass
index (BMI) is 30 kg/m
2
or more, is a global public
health crisis leading to increased prevalence of diabetes
at an unprecedented scale and an associated increased
risk of cardiovascular disease [1].
There is considerable inter-individual variation in
how, where, and to what extent fat deposits around
the body. For example, two individuals can have the
exact same height and weight (that is, identical BMI,
a crude measure of adiposity calculated by dividing
weight by height squared) but have different cardio-
metabolic disease risk (Fig. 1) [1]. These differences
may arise due to where fat is stored. For example, fat
deposited around viscera (proxied by the measure of
an individuals waist-to-hip ratio (WHR)) may have
different impacts on health compared to fat deposited
subcutaneously or around the thighs [2]. Understanding
the relationship between adiposity and disease and the
mechanism(s) by which this relationship is mediated is
critical if we are to find effective approaches to disease
prevention.
* Correspondence: michael.holmes@ndph.ox.ac.uk;celi@well.ox.ac.uk
1
Medical Research Council Population Health Research Unit at the University
of Oxford, Oxford, UK
4
National Institute for Health Research, Oxford Biomedical Research Centre,
Oxford University Hospital, Oxford, UK
Full list of author information is available at the end of the article
Associative and causal relationships
Observational studies provide strong evidence of positive
associations between adiposity and cardiometabolic dis-
ease risk, but can suffer from bias and confounding.
Randomized controlled trials (RCTs) are the gold stand-
ard for establishing causality. While the DIRECT trial
provided reliable evidence for reduced type 2 diabetes
(T2D) risk as a consequence of a lifestyle intervention
leading to weight loss, only one RCT (LOOK-Ahead)
has investigated the clinical impact of reduced caloric in-
take and increased physical activity on CVD risk, but
this was stopped after 10 years due to a lack of efficacy.
Recent studies of cardiometabolic disease have embraced
an alternative approach: Mendelian randomization (MR),
which exploits properties of the genome to make causal,
rather than correlative, inferences on the relationship be-
tween an exposure and an outcome [3].
Initial MR studies in cardiometabolic disease fo-
cused on only a small number of variants associated
with BMI or other adipose-related traits. Studies test-
ing only a small number of single-nucleotide poly-
morphisms (SNPs) are potentially limited as BMI is a
complex trait; a single locus is unlikely to provide a
comprehensive proxy of a traits overall genetic
architecture, which is probably comprised of hundreds
of modest effect associations. Additionally, the identi-
fied locus may be pleiotropic with other traits [3].
Thus, early MR studies were at least in part
hampered by inadequate numbers of genetic variants
(limiting the proportion of explained BMI variance)
and lack sufficient numbers of disease cases (limiting
statistical power). Consequently, early MR studies
yielded unreliable estimates, exemplified by an MR
using 14 SNPs associated with BMI, coupled with a
meta-analysis [4] of the available literature at the
time; while showing robust associations with markers
of inflammation, blood pressure, and diabetes, the
study failed to identify the causal relationship between
BMI and coronary heart disease (CHD) that more
recent work [2] indicates is likely real.
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
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Holmes et al. Genome Medicine (2017) 9:82
DOI 10.1186/s13073-017-0474-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The availability of large-scale genome-wide association
study (GWAS) data generated from increasingly larger
sample sizes [5, 6] has spurred additional methodological
developments in MR. One such advance is two-sample
MR, which exploits separate datasets for the SNP-to-
exposure and SNP-to-outcome relationships, facilitat-
ing the inclusion of GWAS summary data into the
analysis, and thus vastly increasing statistical power.
A second advance has been the increase in the num-
ber of phenotype-associated SNPs, made possible by
collaborative GWAS employing large samples and
dense imputation reference panels (which facilitate
the imputation of unobserved genotypes in the sam-
ples) [5, 6]. These developments in the field allowed,
for example, two-sample MR analysis of 32 BMI SNPs
and data from the Coronary Artery Disease Genome-
wide Replication and Meta-analysis (CARDIoGRAM)
plus the Coronary Artery Disease (C4D) Genetics
consortium (CARDIoGRAMplusC4D) [7] to provide
evidence that adiposity is causally implicated in the
development of CHD. Notably, a one-sample MR ana-
lysis in the same study failed to detect the causal ef-
fect of BMI on incident CHD (highlighting the
importance of adequate statistical power to obtain re-
liable estimates of effect).
Recent MR approaches are elucidating how distinct
features of adiposity causally influence cardiometa-
bolic disease risk through specific mechanisms. One
study [8] used 32 genetic variants to investigate the
effects of BMI on circulating blood-based metabolic
markers, including inflammatory markers and a num-
ber of hormones including leptin and insulin. This
demonstrates how GWAS data and MR can be inte-
grated to implicate potential mediators of the rela-
tionship between BMI and cardiometabolic disease.
The latest obesity GWAS, one examining BMI (which
measures overall fat) and one looking at WHR
adjusted for BMI (WHRadjBMI, which measures cen-
tral adiposity), identified 97 and 49 common variants,
respectively [5, 6]. Notably, these two GWAS reveal
partially distinct genetic signatures in BMI and
WHRadjBMI, prompting the question of whether cen-
tral body fat has effects on cardiometabolic disease
that are independent of total body fat. To address this
question, recent studies [2, 9] used BMI and
WHRadjBMI SNPs to show that, in addition to BMI,
body fat distribution (measured by WHRadjBMI) in-
fluences cardiovascular risk factors (including lipids,
blood pressure, and diabetes), and is potentially more
important than BMI in the development of subclinical
atherosclerosis and stroke [2].
Recent MR analyses using GWAS data also indicate
that insulin resistance (IR) and related measures may
mediate the relationship between adiposity and car-
diometabolic disease. One such effort generated a
genetic proxy for IR based on meta-analysis of prior
Mendelian randomization
using associated genetic markers
Blood pressure
Blood markers of insulin resistance
DNA methylation
Diabetes
Coronary heart disease
Ischaemic stroke
Test for adverse effects on:
16
12
8
4
0
16
12
8
4
0
Chromosome Chromosome
Genome-wide association study (WHRadjBMI)Genome - wide association study (BMI)
Mendelian randomization
using associated genetic
markers
Higher general adiposity Higher central adiposity
Studied phenotype: BMI Studied phenotype: WHRadjBMI
Fig. 1 Relationships of general and central adiposity with cardiometabolic diseases and related traits identified through genetic and epigenetic studies.
Two common phenotypes have been key to the genetic study of adiposity in humans: body mass index (BMI;inblue), which measures general adiposity;
and waist-to-hip ratio adjusted for BMI (WHRadjBMI;inred), which captures central adiposity (that is, fat that collects around the central region of the body
and may mark visceral fat deposits). Genome-wide association studies (GWASs) in BMI and WHRadjBMI have revealed 97 and 49 common variant loci,
respectively, associated with the traits. While GWASs provide evidence for association between genetic variants and phenotypic outcomes, the variants
implicated in these studies can be used in Mendelian randomization (MR) analyses to investigate causal relationships. MR studies using BMI-associated
single nucleotide polymorphisms (SNPs) have established causal relationships of BMI on blood pressure, insulin resistance, DNA methylation (that is,
alterations in gene expression), diabetes, and coronary heart disease. Similar studies, but for WHRadjBMI-associated SNPs, show similar causal relationships
(excluding that for DNA methylation), and a causal role in stroke. The results indicate that not only general adiposity (indexed by BMI) but the distribution
of adipose tissue in particular depots (indexed by WHRadjBMI) is crucial to the relationship between adiposity and cardiometabolic disease outcome
Holmes et al. Genome Medicine (2017) 9:82 Page 2 of 4
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GWAS for triglycerides, high-density lipoprotein
(HDL), and fasting insulin [10], and identified 53 as-
sociated SNPs. Moreover, a genetic instrument com-
posed of those SNPs showed associations with risk of
diabetes and cardiovascular disease (CVD), suggesting an
IR-mediated relationship between adiposity and cardiomet-
abolic disease. The genetic instrument was also associated
with lower peripheral (that is, subcutaneous) adiposity,
which could be interpreted as perturbed subcutaneous
fat distribution playing a role in IR-related cardiomet-
abolic disease. However, the choice to condition on
BMI in one of the primary phenotypes (fasting glu-
cose) may have induced an inverse relationship in the
downstream analysis of IR-related SNPs with periph-
eral adiposity. Additional analyses, with and without
conditioning on BMI, will be necessary to fully eluci-
date this relationship. Because these SNPs are drawn
from summary data of GWAS based on three different
traits (rather than from GWAS of an IR-defined trait),
they may not provide a complete reflection of IR biology.
Additionally, variants identified from such an analysis
(combining three traits in a GWAS meta-analysis)
may be pleiotropic; pleiotropic SNPs may affect mul-
tiple discrete pathways, potentially leading to biased
estimates of disease risk [3]. Despite these caveats,
creating a genetic proxy for IR represents an interest-
ing approach for exploiting existing large-scale GWAS
data to elucidate mediators of adiposity in cardiomet-
abolic disease.
In addition to genetic variation, DNA methylation
mayalsoplayanimportantroleinthedevelopment
of disease by modifying gene expression. A recent
analysis of 5387 samples [11] sought to examine
whether DNA methylation acts as a mediator between
adiposity and cardiometabolic disease. After identify-
ing 187 BMI-associated CpG sites (positions in the
DNA where methylation may occur), the authors per-
formed bidirectional MR, testing whether methylation
changes cause BMI or vice versa. The results indicated that
altered DNA methylation was a consequencerather than
a causeof increased adiposity. Furthermore, the authors
created genetic scores for a number of metabolic markers
of BMI (blood pressure, hemoglobin A1c, HDL cholesterol,
and insulin) and identified that these metabolic markers
also influenced the 187 CpG sites (as opposed to
methylation influencing the markers). These findings
provide tantalizing evidence that, in addition to
considering a standard etiologic framework (in which
BMI increases systolic blood pressure, and higher
systolic blood pressure increases risk of CVD), we
should consider the possibility that BMI and other
cardiometabolic traits may individually or collectively
impact DNA methylation, thereby potentially causing
disease through gene expression.
Conclusions and implications for medicine
Collectively, genetic and genomic studies combined
with MR have provided invaluable evidence that (1)
general and central adiposity almost certainly have
causal roles in the development of cardiometabolic
disease; (2) a causal role for adiposity traits in the de-
velopment of stroke subtypes is emerging [2]; and (3)
potential mediators of adiposity in cardiometabolic
disease, in addition to conventional risk factors like
blood pressure, include insulin resistance, DNA
methylation, and blood-based metabolites [8]. While
these studies have yielded unique insights, challenges
remain. For example, while GWAS have implicated
genomic loci harboring risk variants, they cannot pin-
point the causal genes or mechanisms. Furthermore,
GWAS only interrogate common variation, leaving
rare variants essentially untested (and therefore
under-represented in MR). Importantly, these limita-
tions of GWAS do not necessarily hamper the
applications of GWAS to MR. However, RCTs will,
where feasible, almost always be necessary to establish
robust evidence for the causality and efficacy of po-
tential therapies, prior to the clinical implementation
of findings from MR.
Despite these limitations, recent genetic and epigen-
etic findings have advanced our understanding of dis-
ease etiology and informed new research lines. Key
questions remain, such as whether the BMImethyla-
tion relationship represents a mechanism by which
obese adults pass on harmful cardiometabolic risk to
(lean) children, or whether the associations of BMI
with multiple blood-based metabolites implicates
markers that may represent potential drug targets.
The challenge lies in addressing these questions, and
translating the biological findings discussed here into
effective therapies to combat obesity and the resulting
health complications. As most drug targets are pro-
teins, a natural extension is to investigate the associa-
tions of adiposity-related genetic risk scores with
proteomics at scale. Aligning such findings to re-
sources such as Open Targets (https://www.opentar-
gets.org), a platform that integrates genomic data on
genes and proteins with therapeutic relevance, may
help to prioritize targets to take forward into clinical
trials. Using genetics and epigenetics to identify ther-
apies that can halt or ameliorate the mechanism by
which adiposity leads to cardiometabolic disease will
likely be more efficacious than (or at the very least
enhance) current conventional advice to address
adiposity through improved diet or physical activity,
advice that has had minimal impact on deleterious
global adiposity trends and its consequences.
In summary, genetic and epigenetic studies have
contributed to our understanding of the role of
Holmes et al. Genome Medicine (2017) 9:82 Page 3 of 4
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adiposity in cardiometabolic disease and illuminated
potential mechanisms. Although the field has yet to
find a pivotal drug target from genetic studies, as has
occurred for the PCSK9 gene in CVD treatment, in-
sights from recent efforts provide promising paths
forward that could result in substantial public health
gains for global communities increasingly affected by
obesity and its sequelae.
Abbreviations
BMI: Body mass index; CHD: Coronary heart disease; CVD: Cardiovascular
disease; GWAS: Genome-wide association study; HDL: High-density
lipoprotein; IR: Insulin resistance; MR: Mendelian randomization; SNP: Single
nucleotide polymorphism; WHR: Waist-to-hip ratio; WHRadjBMI: Waist-to-hip
ratio, adjusted for BMI
Authorscontributions
MVH, SLP, and CML all helped to write this manuscript. All authors read and
approved the final manuscript.
Funding
CML is supported by the Li Ka Shing Foundation, National Institutes of
Health (NIH) grant CRR00070 CR00.01 and by the National Institute for Health
Research (NIHR) Biomedical Research Centre, Oxford. SLP is supported by the
Li Ka Shing Foundation. MVH works in a unit that receives funding from the
UK Medical Research Council. This work was supported by the NIHR
Biomedical Research Centre, Oxford.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Medical Research Council Population Health Research Unit at the University
of Oxford, Oxford, UK.
2
Clinical Trial Service Unit & Epidemiological Studies
Unit (CTSU), Nuffield Department of Population Health, University of Oxford,
Oxford, UK.
3
Medical Research Council Integrative Epidemiology Unit,
University of Bristol, Bristol, UK.
4
National Institute for Health Research, Oxford
Biomedical Research Centre, Oxford University Hospital, Oxford, UK.
5
The Big
Data Institute, Li Ka Shing Centre for Health Information and Discovery,
University of Oxford, Oxford, UK.
6
Medical Population and Genetics Program,
Broad Institute, Cambridge, MA, USA.
7
Department of Genetics, University
Medical Center Utrecht, Utrecht, The Netherlands.
8
Wellcome Trust Center for
Human Genetics, Oxford University, Oxford, UK.
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... Nevertheless, a critical question is the mode that FAs conduct the inflammation-related cells to this particular phenotype contributing to the regulation of inflammation. Second, there are increasing inflammation-related pathogenic situations, such as obesity or metabolic syndrome, in which an abnormal epigenetic pattern has been observed [18,29]. DNA methylation may modulate mediators of inflammation including immune cells and inflammatory molecules [13,39] affecting the balance toward a pro-or anti-inflammatory milieu. ...
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Fatty acids (FAs) are known to participate in body inflammatory responses. In particular, saturated FAs such as palmitic acid (PA) induce inflammatory signals in macrophages, whereas polyunsaturated FAs, including docosahexaenoic acid (DHA), have been related to anti-inflammatory effects. Several studies have suggested a role of fatty acids on DNA methylation, epigenetically regulating gene expression in inflammation processes. Therefore, this study investigated the effect of PA and DHA on the inflammation-related genes on human macrophages. In addition, a second aim was to study the epigenetic mechanism underlying the effect of FAs on the inflammatory response. For these purposes, human acute monocytic leukaemia cells (THP-1) were differentiated into macrophages with 12-O-tetradecanoylphorbol-13-acetate (TPA), followed by an incubation with PA or DHA. At the end of the experiment, mRNA expression, protein secretion, and CpG methylation of the following inflammatory genes were analysed: interleukin 1 beta (IL1B), tumour necrosis factor (TNF), plasminogen activator inhibitor-1 (SERPINE1) and interleukin 18 (IL18). The results showed that the treatment with PA increased IL-18 and TNF-α production. Contrariwise, the supplementation with DHA reduced IL-18, TNF-α and PAI-1 secretion by macrophages. However, the incubation with these fatty acids did not apparently modify the DNA methylation status of the investigated genes in the screened CpG sites. This research reveals that PA induces important pro-inflammatory markers in human macrophages, whereas DHA decreases the inflammatory response. Apparently, DNA methylation is not directly involved in the fatty acid-mediated regulation of the expression of these inflammation-related genes.
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The escalating prevalence of individuals becoming overweight and obese is a rapidly rising global health problem, placing an enormous burden on health and economic systems worldwide. Whilst obesity has well described lifestyle drivers, there is also a significant and poorly understood component that is regulated by genetics. Furthermore, there is clear evidence for sexual dimorphism in obesity, where overall risk, degree, subtype and potential complications arising from obesity all differ between males and females. The molecular mechanisms that dictate these sex differences remain mostly uncharacterised. Many studies have demonstrated that this dimorphism is unable to be solely explained by changes in hormones and their nuclear receptors alone, and instead manifests from coordinated and highly regulated gene networks, both during development and throughout life. As we acquire more knowledge in this area from approaches such as large-scale genomic association studies, the more we appreciate the true complexity and heterogeneity of obesity. Nevertheless, over the past two decades, researchers have made enormous progress in this field, and some consistent and robust mechanisms continue to be established. In this review, we will discuss some of the proposed mechanisms underlying sexual dimorphism in obesity, and discuss some of the key regulators that influence this phenomenon.
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Cordycepin is an extract from the insect fungus Cordyceps. militaris, which is a traditional medicine with various biological function. In previous studies, cordycepin had been reported with excellent anti-obesity effect, but the mechanism is unclear. A large quantity of evidences showed that prolactin plays an important part in body weight regulation, hyperprolactinemia can promote appetite and accelerate fat deposition. In this study, we explored the molecular mechanism of the anti-obesity effect of cordycepin by reducing prolactin release via an adenosine A1 receptor. In vivo, obese rats model was induced by high fat diet for 5 weeks, the serum and liver lipids coupling with serum prolactin were reduced by treatment of cordycepin, the results suggested that cordycepin is a potential drug for therapying obesity which could be related with prolactin. In vitro, cordycepin could inhibit prolactin secretion in GH3 cells via upregulating the expression of adenosine A1 receptor, the inhibition effect could be blocked by an antagonist of adenosine receptor A1 DPDPX, prolactin induced the upregulation of lipogenesis genes PRLR, and P-JAK2 in 3T3-L1 cells. Intriguingly, cordycepin would down-regulate the expression of prolactin receptor (PRLR). Thus, we concluded that cordycepin modulate body weight by reducing prolactin release via an adenosine A1 receptor.
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Antecedentes: Los índices aterogénicos son indicadores bioquímicos que suelen relaciónarse con la adiposidad corporal y con el desarrollo de enfermedades cardiometabólicas, las cuales representan un serio problema en salud pública, no sólo por la morbimortalidad asociada, sino por el gasto sanitario que conlleva, situación que no resulta ajena en la población militar. Objetivo: Determinar si el índice de masa corporal en cadetes colombianos con sobrepeso se debe a masa magra o grasa y si se relacióna con los índices aterogénicos. Materiales y métodos: Estudio descriptivo - observaciónal, en cadetes con sobrepeso, valorados en el centro de investigaciónes de la cultura física (CICFI) de la Escuela militar de cadetes “General José María Córdova”. Para el análisis de la información se empleó el paquete estadístico SPSS 24, aplicando pruebas de normalidad, estadísticos descriptivos para datos de comportamiento normal y correlación de Pearson. Resultados: 90 cadetes con edad promedio de 22,0 ± 3,0 años e índice de masa corporal en 27,3 ± 1,8 kg/m², fueron incluidos, 58,0% hombres y 32,0% mujeres. El índice preaterogénico se relaciónó con el IMC (r= 0,305 p=0,02), el índice de Castelli se relaciónó, débilmente con el índice de masa corporal (r=0,254 p=0,05). En ningún grupo, el cociente de Triglicéridos se relaciónó con el IMC. Conclusiones: Los índices aterogénicos en cadetes con sobrepeso, se encuentran en rangos de normalidad y presentan relaciónes débiles con el índice de masa corporal. El sobrepeso se debe a mayor masa magra.
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Functions in all tissues depend on proteins that are produced through the process of transcription of genes, i.e., gene expression. The regulation of gene expression in the human body is a complex network that encompasses the entire central dogma of cell biology. Gene expression can be regulated at the transcriptomic level through epigenetic silencing of genes as well as through the promotion of gene expression with the binding of transcription factors. Gene expression can also be altered following the production of mature messenger RNA transcripts. This chapter will detail genes which are misregulated in cardiometabolic disease and how the aberrant expression of these genes results in metabolic issues. Additionally, we highlight that exercise can positively influence several of the same molecular pathways that are negatively affected in CMD. Furthermore, we discuss how these beneficial changes are all done through the regulation of gene expression and how differences between individuals can influence the effects of exercise.
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Excessive fat deposition in obesity has a multifactorial aetiology, but is widely considered the result of disequilibrium between energy intake and expenditure. Despite specific public health policies and individual treatment efforts to combat the obesity epidemic, >2 billion people worldwide are overweight or obese. The central nervous system circuitry, fuel turnover and metabolism as well as adipose tissue homeostasis are important to comprehend excessive weight gain and associated comorbidities. Obesity has a profound impact on quality of life, even in seemingly healthy individuals. Diet, physical activity or exercise and lifestyle changes are the cornerstones of obesity treatment, but medical treatment and bariatric surgery are becoming important. Family history, food environment, cultural preferences, adverse reactions to food, perinatal nutrition, previous or current diseases and physical activity patterns are relevant aspects for the health care professional to consider when treating the individual with obesity. Clinicians and other health care professionals are often ill-equipped to address the important environmental and socioeconomic drivers of the current obesity epidemic. Finally, understanding the epigenetic and genetic factors as well as metabolic pathways that take advantage of 'omics' technologies could play a very relevant part in combating obesity within a precision approach. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
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Mendelian randomization (MR) is a burgeoning field that involves the use of genetic variants to assess causal relationships between exposures and outcomes. MR studies can be straightforward; for example, genetic variants within or near the encoding locus that is associated with protein concentrations can help to assess their causal role in disease. However, a more complex relationship between the genetic variants and an exposure can make findings from MR more difficult to interpret. In this Review, we describe some of these challenges in interpreting MR analyses, including those from studies using genetic variants to assess causality of multiple traits (such as branched-chain amino acids and risk of diabetes mellitus); studies describing pleiotropic variants (for example, C-reactive protein and its contribution to coronary heart disease); and those investigating variants that disrupt normal function of an exposure (for example, HDL cholesterol or IL-6 and coronary heart disease). Furthermore, MR studies on variants that encode enzymes responsible for the metabolism of an exposure (such as alcohol) are discussed, in addition to those assessing the effects of variants on time-dependent exposures (extracellular superoxide dismutase), cumulative exposures (LDL cholesterol), and overlapping exposures (triglycerides and non-HDL cholesterol). We elaborate on the molecular features of each relationship, and provide explanations for the likely causal associations. In doing so, we hope to contribute towards more reliable evaluations of MR findings.
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Importance In observational studies, abdominal adiposity has been associated with type 2 diabetes and coronary heart disease (CHD). Whether these associations represent causal relationships remains uncertain. Objective To test the association of a polygenic risk score for waist-to-hip ratio (WHR) adjusted for body mass index (BMI), a measure of abdominal adiposity, with type 2 diabetes and CHD through the potential intermediates of blood lipids, blood pressure, and glycemic phenotypes. Design, Setting, and Participants A polygenic risk score for WHR adjusted for BMI, a measure of genetic predisposition to abdominal adiposity, was constructed with 48 single-nucleotide polymorphisms. The association of this score with cardiometabolic traits, type 2 diabetes, and CHD was tested in a mendelian randomization analysis that combined case-control and cross-sectional data sets. Estimates for cardiometabolic traits were based on a combined data set consisting of summary results from 4 genome-wide association studies conducted from 2007 to 2015, including up to 322 154 participants, as well as individual-level, cross-sectional data from the UK Biobank collected from 2007-2011, including 111 986 individuals. Estimates for type 2 diabetes and CHD were derived from summary statistics of 2 separate genome-wide association studies conducted from 2007 to 2015 and including 149 821 individuals and 184 305 individuals, respectively, combined with individual-level data from the UK Biobank. Exposures Genetic predisposition to increased WHR adjusted for BMI. Main Outcomes and Measures Type 2 diabetes and CHD. Results Among 111 986 individuals in the UK Biobank, the mean age was 57 (SD, 8) years, 58 845 participants (52.5%) were women, and mean WHR was 0.875. Analysis of summary-level genome-wide association study results and individual-level UK Biobank data demonstrated that a 1-SD increase in WHR adjusted for BMI mediated by the polygenic risk score was associated with 27-mg/dL higher triglyceride levels, 4.1-mg/dL higher 2-hour glucose levels, and 2.1–mm Hg higher systolic blood pressure (each P < .001). A 1-SD genetic increase in WHR adjusted for BMI was also associated with a higher risk of type 2 diabetes (odds ratio, 1.77 [95% CI, 1.57-2.00]; absolute risk increase per 1000 participant-years, 6.0 [95% CI, CI, 4.4-7.8]; number of participants with type 2 diabetes outcome, 40 530) and CHD (odds ratio, 1.46 [95% CI, 1.32-1.62]; absolute risk increase per 1000 participant-years, 1.8 [95% CI, 1.3-2.4]; number of participants with CHD outcome, 66 440). Conclusions and Relevance A genetic predisposition to higher waist-to-hip ratio adjusted for body mass index was associated with increased risk of type 2 diabetes and coronary heart disease. These results provide evidence supportive of a causal association between abdominal adiposity and these outcomes.
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Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation, a key regulator of gene expression and molecular phenotype. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10−7, range P = 9.2 × 10−8 to 6.0 × 10−46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10−6, range P = 5.5 × 10−6 to 6.1 × 10−35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07–2.56); P = 1.1 × 10−54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.
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Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.
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Adiposity, as indicated by body mass index (BMI), has been associated with risk of cardiovascular diseases in epidemiological studies. We aimed to investigate if these associations are causal, using Mendelian randomization (MR) methods. The associations of BMI with cardiovascular outcomes [coronary heart disease (CHD), heart failure and ischaemic stroke], and associations of a genetic score (32 BMI single nucleotide polymorphisms) with BMI and cardiovascular outcomes were examined in up to 22 193 individuals with 3062 incident cardiovascular events from nine prospective follow-up studies within the ENGAGE consortium. We used random-effects meta-analysis in an MR framework to provide causal estimates of the effect of adiposity on cardiovascular outcomes. There was a strong association between BMI and incident CHD (HR = 1.20 per SD-increase of BMI, 95% CI, 1.12-1.28, P = 1.9·10(-7)), heart failure (HR = 1.47, 95% CI, 1.35-1.60, P = 9·10(-19)) and ischaemic stroke (HR = 1.15, 95% CI, 1.06-1.24, P = 0.0008) in observational analyses. The genetic score was robustly associated with BMI (β = 0.030 SD-increase of BMI per additional allele, 95% CI, 0.028-0.033, P = 3·10(-107)). Analyses indicated a causal effect of adiposity on development of heart failure (HR = 1.93 per SD-increase of BMI, 95% CI, 1.12-3.30, P = 0.017) and ischaemic stroke (HR = 1.83, 95% CI, 1.05-3.20, P = 0.034). Additional cross-sectional analyses using both ENGAGE and CARDIoGRAMplusC4D data showed a causal effect of adiposity on CHD. Using MR methods, we provide support for the hypothesis that adiposity causes CHD, heart failure and, previously not demonstrated, ischaemic stroke. © The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
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Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 x 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for approximately 2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis
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Background—Implications of different adiposity measures on cardiovascular disease aetiology remain unclear. In this paper we quantify and contrast causal associations of central adiposity (waist:hip ratio adjusted for BMI (WHRadjBMI)) and general adiposity (body mass index (BMI)) with cardiometabolic disease. Methods—97 independent single nucleotide polymorphisms (SNPs) for BMI and 49 SNPs for WHRadjBMI were used to conduct Mendelian randomization analyses in 14 prospective studies supplemented with CHD data from CARDIoGRAMplusC4D (combined total 66,842 cases), stroke from METASTROKE (12,389 ischaemic stroke cases), type 2 diabetes (T2D) from DIAGRAM (34,840 cases), and lipids from GLGC (213,500 participants) consortia. Primary outcomes were CHD, T2D, and major stroke subtypes; secondary analyses included 18 cardiometabolic traits. Results—Each one standard deviation (SD) higher WHRadjBMI (1SD~0.08 units) associated with a 48% excess risk of CHD (odds ratio [OR] for CHD: 1.48; 95%CI: 1.28-1.71), similar to findings for BMI (1SD~4.6kg/m2; OR for CHD: 1.36; 95%CI: 1.22-1.52). Only WHRadjBMI increased risk of ischaemic stroke (OR 1.32; 95%CI 1.03-1.70). For T2D, both measures had large effects: OR 1.82 (95%CI 1.38-2.42) and OR 1.98 (95%CI 1.41-2.78) per 1SD higher WHRadjBMI and BMI respectively. Both WHRadjBMI and BMI were associated with higher left ventricular hypertrophy, glycaemic traits, interleukin-6, and circulating lipids. WHRadjBMI was also associated with higher carotid intima-media thickness (39%; 95%CI: 9%-77% per 1SD). Conclusions—Both general and central adiposity have causal effects on CHD and T2D. Central adiposity may have a stronger effect on stroke risk. Future estimates of the burden of adiposity on health should include measures of central and general adiposity.
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Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.
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Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.