Epigenetic underpinnings of
Adiposity
Oct 8, 2015
Golareh Agha, PhD
Harvard School of Public Health
Dept. of Environmental Health
gagha@hsph.harvard.edu
1
Outline
 Definitions and background
 Review of prior literature on epigenetics and adiposity
• Established evidence from animal studies and Dutch
Hunger study
• More recent epidemiological studies (discussion of
limitations)
 Example of more recent research efforts that address
prior limitations
• Lancet study on BMI
• My PhD paper (as a case study)
• Our lab’s current contribution to PACE consortium
2
Adiposity
 Definition: Excess body fat, including total body
obesity as well as regional fat and body fat
distribution
 Established risk factor for numerous chronic
diseases, such as type 2 diabetes and
cardiovascular disease
• Total healthcare costs attributable to obesity could
reach $957 billion by 2030
 The etiology of adiposity is multifactorial
• Structural (e.g. food policy, school lunch programs),
social (e.g. education, social network), behavioral (e.g.
diet, physical activity), genetic (e.g. SNPs), ……
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Epigenetic mechanisms underlying
adiposity?
 There is a need to better understand:
• Mechanisms by which environmental and genetic
factors contribute to obesity
• The underlying molecular mechanisms in
development of adiposity
 Adiposity and related cardiometabolic risk may
arise as a result of dysregulated cellular
programming and alterations of regulatory
pathways via epigenetic mechanisms 1,2
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Epigenetics
 Mechanisms that contribute to the regulation of gene
expression states
 DNA methylation
• Plays a central role during organismal development, whereby genomewide de novo methylation after implantation leads to tissue-specific
DNA methylation patterns that then influence cellular differentiation
in the developing organism
http://epigenie.com/epigenetics/epigenetic-regulation/
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Early life exposures, DNA methylation,
adiposity
Animal studies1,2,3,4
 In sheep: maternal folate deficiency led to CpG island DNA
methylation changes and weight gain in offspring
 In rats: protein restriction reduced DNA methylation in
metabolic regulatory genes (PPARa, GR1) in offspring
 In rats: maternal high fat diet led to hypomethylation, DNA
methylation changes in appetite-regulatory genes,
adiposity in offspring
Human studies
 In utero exposure to famine5,6,7 associated with:
• Offspring blood DNA methylation changes in genes implicated in
growth and metabolic disease: e.g. IGF2, LEP 8,9
• Increased BMI and waist circumference
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DNA methylation and Adiposity
 Wang et al. Obesity related methylation changes in DNA of peripheral blood
leukocytes. BMC Med. 2010
 Feinberg et al. Personalized epigenomic signatures that are stable over time and
covary with body mass index. Sci Transl Med. 2010
 Kuehnen et al. An Alu element-associated hypermethylation variant of the POMC
gene is associated with childhood obesity. PLoS Genet.2012
 Zhao et al. Promoter methylation of serotonin transporter gene is associated with
obesity measures: a monozygotic twin study. Int J Obes (Lond). 2012.
 Groom et al. Postnatal growth and DNA methylation are associated with differential
gene expression of the TACSTD2 gene and childhood fat mass. Diabetes.2012
 Godfrey et al. Epigenetic gene promoter methylation at birth is associated with
child's later adiposity. Diabetes. 2011
 Relton et al. DNA methylation patterns in cord blood DNA and body size in
childhood. PLoS One. 2012
 Almen et al. Genome wide analysis reveals association of a FTO gene variant with
epigenetic changes. Genomics. Mar 2012
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Limitations of most prior studies:
 Focus on highly selective genomic regions
• E.g. promoter regions, a priori selected genes, or
cancer Panel array (heavily biased towards cancer
genes)
 Focus on blood as the tissue source
• Given that DNA methylation is a tissue-specific
phenomenon, there is considerable interest in
investigating more targeted tissues (e.g. adipose)
 Small sample size!
8
Dick KJ, Nelson CP, Tsaprouni L, et al. DNA methylation and body-mass
index: a genome-wide analysis. Lancet 2014
9
Dick KJ, Nelson CP, Tsaprouni L, et al. DNA methylation and body-mass
index: a genome-wide analysis. Lancet 2014
10
Dick KJ, Nelson CP, Tsaprouni L, et al. DNA methylation and body-mass
index: a genome-wide analysis. Lancet 2014
HIF3A
 Encodes a protein that is one component of the heterodimeric hypoxiainducible factor (HIF) transcriptional complex
• regulates many adaptive responses to hypoxia
• adipocyte-specific targeted disruption of other genes (HIF1A and
ARNT) in the HIF heterodimer previously associated with reduced
fat formation and insulin resistance in transgenic mice fed a high-fat
diet, in comparison with wild-type control mice who were also fed a
high-fat diet
11
Agha G, Houseman EA, Kelsey KT, Eaton CB, Buka SL, Loucks EB.
Adiposity is associated with DNA methylation profile in adipose tissue.
Int J Epidemiol. 2014
 To examine whether epigenome-wide DNA
methylation profiles in blood and adipose
tissue are associated with measures of
adiposity, including:
• Central adiposity
• Body fat distribution
• Body mass index
12
Karpe, F. & Pinnick, K. E. Nat. Rev. Endocrinol. 11, 90–100 (2015)
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The Longitudinal Effects on Aging
Perinatal (LEAP) Project
 Originated from the Collaborative Perinatal
Project (CPP)
• ~60,000 pregnant women were recruited in 19581965 across the US
• Regular assessments on mothers and offspring
were performed until offspring age 7 years
 For LEAP: providence-born offspring of CPP
moms were sampled
• 400 participants enrolled
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The Longitudinal Effects on Aging Perinatal
(LEAP) project
Collaborative Perinatal Project (n=~60,000)
In
Utero
Birth
• Maternal
Cortisol
• gestational
diabetes
• BP
• Smoking
• Socioeconomic
index
• Birth weight
• Fetal length
• Placental
morphology
• Apgar score
Age
1-4
• Weight/
Height
• Cognitive
function
• Parental
bonding
Assessments on
Providence-born
offspring:
LEAP (n=400)
Age 7
• Blood
pressure
• Weight/
Height
•Cognitive
function
• Parental
SEP
Age
44-50
LEAP
Epigenetic
subsample
(n=108)
Age
44-50
•Subcutaneous
adipose tissue /
blood tissue
• DEXA measures
• Carotid IMT
• Metabolic
syndrome
• Lipids
•CRP
DNA
methylation
data in
blood and
adipose
tissue
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Methods
Study sample
 106 participants (68 women, 38 men), aged 44-50 yrs
Measures of Adiposity:
 Dual-energy x-ray
absorptiometry
(DXA) scans measures:
• android fat mass
• android:gynoid fat ratio
• trunk:limb fat ratio
 BMI (measured height
and weight)
Covariates of interest
 Race - 72 white; 34 nonwhite
 Current smokers- 34%
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Methods
Tissue samples
 Blood
• Peripheral blood leukocytes extracted from buffy coats
 Subcutaneous adipose tissue
• collected from the upper outer quadrant of the buttock
using a 16-gauge needle and disposable syringe
Methylation profiling
 DNA extracted, bisulfite converted, and analyzed using
the Infinium HumanMethylation450 BeadChip array
 Methylation status at each CpG (“average beta”) =
M/(M+U+ε)
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Methods
Quality control and preprocessing of DNA
methylation data
 Poorly performing probes and samples dropped
• Based on criterion: detection p-val >0.05 for > 1%
 Out-of-band background correction1
 Dye-bias adjustment2
 Beta-Mixture Quantile Dilation (BMIQ)
normalization3
• In order to obtain similar ranges for type I vs. type II
probes on the Infinium array
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Statistical Analyses
 Additional probe removal prior to analysis:
• Ch and rs probes, CpG sites on sex chromosomes
• SNP probes
• Cross-reactive probes
 Average beta values logit-transformed to Mvalues prior to analyses, and adjusted for batch
effects1 (beadchip position on plate)
 Epigenome-wide (EWAS) analyses
• CpG-by-CpG analyses in combination with omnibus
tests for significance via permutation testing
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Statistical Analyses
Analyses in Blood adjusted for cell type composition
 Blood consists of a heterogeneous mixture of different cell
types, each with its own DNA methylation signature1
 Thus, any association observed between DNA methylation
and adiposity could be due to adiposity-associated changes
in cell type proportions (e.g. due to inflammation)2
 Reference-based method (Houseman 20123)
used to estimate proportion of major
leukocyte cell types:
CD8T, CD4T, NK, B-cell, Mon, Gran
• A regression calibration method
that uses a reference dataset with
epigenome profiles on component
cell types, based on flow-sorted
samples
cell type
composition
Adiposity
Observed
DNAm
patterns
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Statistical Analyses
Analyses in adipose tissue also adjusted for cell type composition
 Aside from adipocytes, adipose tissue consists of stromal-vascular
cells such as fibroblastic connective tissue cells, leukocytes, and
macrophages1
 Composition of adipose tissue can shift as a result of adiposity and
related inflammation
 Unlike blood, reference epigenome datasets not available for cell
types in adipose
 Reference-free method2 used to estimate
cell mixture effects in adipose
• Method similar to the surrogate
variable approaches,33,34 thus
producing estimates of non-cell
mixture mediated DNA
methylation associations
21
Agha 2014, Int J Epidemiol
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Agha 2014, Int J Epidemiol
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- AQP7 encodes a glycerol transporter expressed in
adipocytes
- Evidence from both animal and human studies suggest
down-regulation of AQP7 associated with development
of obesity.
- Specifically, AQP7 gene expression shown to be downregulated in subcutaneous adipose tissue of obese vs
lean individuals.
- AOC3 encodes a major protein
that resides on the adipocyte plasma
membrane
- serum levels of the protein predicted
10-year cardiovascular
mortality in type 2 diabetic subjects.
Agha 2014, Int J Epidemiol
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Agha 2014, Int J Epidemiol
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“Parents and Child Epigenetics”
(PACE) consortium
A consortium effort bringing together birth
cohorts from around the world
 Increase sample size by pooling many
cohorts
 Meta-analysis of maternal BMI and cord
blood DNA methylation is currently
underway!
• 13 cohorts participating (total N ~ 6300)
• We are participating in this meta-analysis with
the VIVA cohort
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 Cord blood DNA methylation on ~ 500
mother/child pairs
 Age 3 blood DNA methylation on ~300
children
 Age 7 blood DNA methylation on ~ 400
children
https://www.hms.harvard.edu/viva/
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