C O M M E N T Open Access
Genetic and epigenetic studies of adiposity
and cardiometabolic disease
Michael V. Holmes
, Sara L. Pulit
and Cecilia M. Lindgren
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.
Obesity, an excess of adiposity in which the body mass
index (BMI) is 30 kg/m
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 .
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) . These differences
may arise due to where fat is stored. For example, fat
deposited around viscera (proxied by the measure of
an individual’s waist-to-hip ratio (WHR)) may have
different impacts on health compared to fat deposited
subcutaneously or around the thighs . 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
* Correspondence: firstname.lastname@example.org;email@example.com
Medical Research Council Population Health Research Unit at the University
of Oxford, Oxford, UK
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 .
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 trait’s overall genetic
architecture, which is probably comprised of hundreds
of modest effect associations. Additionally, the identi-
fied locus may be pleiotropic with other traits .
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  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  indicates is likely real.
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Holmes et al. Genome Medicine (2017) 9:82
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)  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  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 .
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
using associated genetic markers
Blood markers of insulin resistance
Coronary heart disease
Test for adverse effects on:
Genome-wide association study (WHRadjBMI)Genome - wide association study (BMI)
using associated genetic
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
GWAS for triglycerides, high-density lipoprotein
(HDL), and fasting insulin , 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 . 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-
In addition to genetic variation, DNA methylation
of disease by modifying gene expression. A recent
analysis of 5387 samples  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 consequence—rather than
a cause—of 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 ; 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 . 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 BMI–methyla-
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
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.
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
MVH, SLP, and CML all helped to write this manuscript. All authors read and
approved the final manuscript.
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.
The authors declare that they have no competing interests.
Medical Research Council Population Health Research Unit at the University
of Oxford, Oxford, UK.
Clinical Trial Service Unit & Epidemiological Studies
Unit (CTSU), Nuffield Department of Population Health, University of Oxford,
Medical Research Council Integrative Epidemiology Unit,
University of Bristol, Bristol, UK.
National Institute for Health Research, Oxford
Biomedical Research Centre, Oxford University Hospital, Oxford, UK.
Data Institute, Li Ka Shing Centre for Health Information and Discovery,
University of Oxford, Oxford, UK.
Medical Population and Genetics Program,
Broad Institute, Cambridge, MA, USA.
Department of Genetics, University
Medical Center Utrecht, Utrecht, The Netherlands.
Wellcome Trust Center for
Human Genetics, Oxford University, Oxford, UK.
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