Paolo Vineis
Imperial College London
And HuGeF Foundation Torino
The use of new omic technologies to
understand the impact of socioeconomic differentials and the
environment on ageing
25 November 2015
Socio-economic status and biomarkers
Hypothalamic-pituitary-adrenal axis
Source: Wolfe B, Evans W,
Seeman T. The biological
consequences of health
inequalities (2012). Russel
Sage Foundation, New York
Cortisol - Saliva, urine
Dehydroepiandrosterone sulfate - Blood
Sympathetic neuro-hormonal system
Norepinephrine/Epinephrine - Urine
Alpha-amylase - Saliva
Parasympathetic neuro-hormonal system Heart rate variability - Pulse rate recording
Inflammatory/Immune system
C-reactive protein- Blood
Erythrocyte sedimentation rate- Blood
Interleukins- Blood
Lymphocyte number and function- Blood
Circulating serum albumin - Blood, saliva
Cardiovascular
Diastolic/systolic blood pressure
Resting heart rate
Glucose metabolism
Fasting glucose- Blood
Glycosylated hemoglobin- Blood
Fasting insulin- Blood
Lipid metabolism
Cholesterol and lipoprotein fractions - Blood
BMI, waist to hip ratio
Total body fat - DXA scan
Hematological
Serum hemoglobin- Blood
Clotting factors and clotting time - Blood
Renal
Creatinine - Serum or 24h urine
Urine albumin leakage - Urine
Cystatin C - Serum or dried blood spot
Hepatic
Circulating serum albumin - Blood, saliva
Reproductive
Serum testosterone/estradiol- Blood
Follicle-stimulating hormone - Blood
Pulmonary
Arterial oxygen saturation - Pulse oximeter
Peak expiratory flow - Spirometer
Bone
Bone density - DXA scan
Bone turnover markers - Blood, fasting urine
Muscle
Skeletal muscle mass - DXA scan, body impedance
Grip strength - Dynamometer
SES and immune system biomarkers
NHANES IV
Alley et al. Socioeconomic status and C-reactive protein levels in the US population: NHANES IV. Brain Behav Immun. 2006
Sep;20(5):498-504
Epigenetics – DNA methylation
Epigenetic modifications
Functionally relevant modifications to the genome that do not
involve a change in the nucleotide sequence. Examples of such
modifications are DNA methylation and histone modification,
both of which serve to regulate gene expression without altering
the underlying DNA sequence.
Gene expression
Phenotype
Dominance rank and expression level of proinflammatory genes (macaques)
Tung et al. Social environment is associated with gene regulatory variation in the rhesus macaque immune system.
Proc Natl Acad Sci U S A. 2012 Apr 24;109(17):6490-5.
SES and DNA methylation – EPIC Turin
• Selection of candidate genes based on literature
review: NR3C1, IL1A, CCL2, CXCL2, CCL20,
GPR132, ADM, OLR1, CREBZF, TNFRSF11A, PTGS2,
CXCR2, NFATC1, SAT2, MTHFR, AHRR, IGF2
• A total of 599 CpG sites were examined.
• Several indicators of socioeconomic status across
the lifecourse
• Adjustment for potential confounding from
lifestyle factors
p_valuesfromlinear regressions
A
Father's occupational position
.0001
.00012438
.001
.01
.05
.1
1
-.01
-.005
0
.005
.01
Mean methylation difference (low vs high SES)
p_valuesfromlinear regressions
B
Household's highest occupational position
1.000e-06
.00001
.0001
.001
.0050995
.01
.05
.1
1
-.02
-.01
0
.01
.02
Mean methylation difference (low vs high SES)
p_valuesfromlinear regressions
C
Lifecourse SES trajectories
1.000e-07
1.000e-06
.00001
.0001
.001
.00149254
.01
Indicators of socioeconomic status
are associated with DNA
methylation of candidate genes.
The graphs represent the plot of
beta coefficients and p-values from
linear regression of CpG sites on
socioeconomic indicators,
adjusted for age, sex, season of
blood collection and disease
status. The red line represents the
corrected overall critical p-value
after a multiple-test procedure
(FDR). Data points on or above the
red line correspond to rejected null
hypotheses (p-values that
remained significant after multipletesting). For household’s highest
occupational position (B)26 data
points are above the red line; for
lifecourse socioeconomic
trajectory (C), 7 data points.
.05
.1
1
-.02
-.01
0
.01
Mean methylation difference (low vs high SES)
.02
Stringhini et al, International
Journal of Epidemiology 2015
A
Household's highest occupational position
*
* *
-0.5
High=0
High=0
High=0
0.0
High=0
0.5
High=0
Δ DNA Methylation (%)
1.0
*
*
*
-1.0
Middle
Low
-1.5
NFATC1
B
MAP3K6
GPR132
IL1A
CXCL2
Lifecourse SES trajectory
-0.5
***
* *
*
High-High=0
High-High=0
High-High=0
0.0
High-High=0
0.5
High-High=0
Δ DNA Methylation (%)
1.0
*
* *
Low-High
-1.0
High-Low
Low-Low
-1.5
NFATC1
MAP3K6
GPR132
IL1A
CXCL2
Preliminary evidence: application of Horvath model of ageing
(biological clock based on methylation) to EPIC-Italy data.
A «socio-molecular» study from
existing cohorts:
LIFEPATH
Participant organisation name
Country
Section Title
Imperial College London - P Vineis (Coordinator), M Ezzati, P Elliott, M Chadeau-Hyam, AC UK
Vergnaud
University College London - M Kivimaki, M Marmot
UK
Lausanne University - S Stringhini, M Bochud
Switzerland
INSERM Toulouse - M Kelly, T Lang, C Delpierre
France
Erasmus University, Rotterdam - J Mackenbach
Netherlands
London School of Economics - M Avendano-Pabon
UK
Columbia University, New York - S Galea, P Muennig
USA
Finnish Institute of Occupational Health, Helsinki - H Alenius, D Greco
Finland
HuGeF Foundation, Torino - GL Severi, S Polidoro
Italy
INSERM Paris - M Goldberg, F Clavel
France
Porto University - H Barros
Portugal
Cancer Council Victoria - G Giles
Australia
ESRI, Dublin - R Layte
Ireland
University of Torino - G Costa, A D’Errico
Italy
Zadig (SME) - R Satolli, L Carra
Italy
Enter text here
www.environment-health.ac.uk
We use the revised Strachan-Sheikh (2004) model of life-course functioning (Kuh D 2007;
Blane et al, 2013), to describe ageing across the life-course. This model presents ageing
as a phenomenon with two broad stages across life: build-up & decline.
Objectives:
To show that healthy ageing is an achievable goal for society, as it
is already experienced by individuals of high socio-economic
status (SES).
To improve the understanding of the mechanisms through which
healthy ageing pathways diverge by SES, by investigating lifecourse biological pathways using omic technologies.
To examine the consequences of the current economic recession
on health and the biology of ageing (and the consequent increase
in social inequalities).
To provide updated, relevant and innovative evidence for healthy
ageing policies (particularly “health in all policies”)
These objectives will be accomplished by using different data sources:
1. Europe-wide and national surveys (updated to 2010), including EU-27.
2. Longitudinal cohorts (across Europe) with intense phenotyping and
repeat biological samples (total population >33,000).
3. Other large cohorts with biological samples (total population >202,000
and a large registry dataset with over a million individuals with very rich
information on work trajectories and health.
4. A randomized experiment on conditional cash transfer for poverty
reduction in New York City.
Data will be harmonized and integrated to conceptualize healthy ageing as a
composite outcome at different stages of life, resulting from life-course
environmental, behavioural and social determinants.
Markers already measured or whose measurement is funded/on-going, by
geographical location of the cohorts and life stage. IM=inflammation markers.
Early life
Geography
Available markers
Young Finns
North (Finland)
2,300 IM
Generacao 21
South (Portugal)
4,500 IM
EPITEEN
South (Portugal)
2,900 IM
Whitehall II
North (UK)
6,600 IM, 10,000 metabolomics
TILDA
North (Ireland)
5,800 IM
Airwave
North (UK)
35,000 IM, 3,000 metabolomics
Skipogh
Centre (Switzerland)
250 methylome and transcriptome, 1,100 IM
Colaus
Centre (Switzerland)
6,300 IM
EPIC Italy
South (Italy)
Methylome>1,000
E3N
South (France)
Metabolome 1,600
Constances
South (France)
35,000 IM
EPIPORTO
South (Portugal)
2,500 IM
MCCS
Australia
Methylome 3,000, IM 500
Late life
A new paradigm for the study of environmental causes of
disease: the EXPOSOME
Relationships between macro-environment and microenvironment
S.M. Rappaport and M.T. Smith, Science, 2010: 330, 460461
IARC: Exposome-Explorer
350 environmental
pollutants
PCBs
All biomarkers
- 497 biomarkers
- 10,480 concentration values
PCDDs
Biomarkers for environmental
pollutants
- 350 biomarkers
- 7,342 concentration values
- 265 publications analyzed
PBDEs
Pesticides
365 concentration
values
PCDFs
PAHs
147 dietary
compounds
POLYPHENOLS
FATTY ACIDS
CAROTENOIDS
-
Chemistry
Cohorts where measured
Biospecimens
Analytical methods
Concentrations
State of validation
Correlations with exposures
Confunding factors
Available on-line
Linked to other databases
19
Thank you