Paolo VineisImperial College London

And HuGeF Foundation Torino

The use of new omic technologies tounderstand the impact of socio-economic differentials and the

environment on ageing

25 November 2015

Socio-economic status and biomarkers

Hypothalamic-pituitary-adrenal axis Cortisol - Saliva, urineDehydroepiandrosterone sulfate - Blood

Sympathetic neuro-hormonal system Norepinephrine/Epinephrine - UrineAlpha-amylase - Saliva

Parasympathetic neuro-hormonal system Heart rate variability - Pulse rate recordingInflammatory/Immune system C-reactive protein- Blood

Erythrocyte sedimentation rate- BloodInterleukins- BloodLymphocyte number and function- BloodCirculating serum albumin - Blood, saliva

Cardiovascular Diastolic/systolic blood pressureResting heart rate

Glucose metabolism Fasting glucose- BloodGlycosylated hemoglobin- BloodFasting insulin- Blood

Lipid metabolism Cholesterol and lipoprotein fractions - BloodBMI, waist to hip ratioTotal body fat - DXA scan

Hematological Serum hemoglobin- BloodClotting factors and clotting time - Blood

Renal Creatinine - Serum or 24h urineUrine albumin leakage - UrineCystatin C - Serum or dried blood spot

Hepatic Circulating serum albumin - Blood, salivaReproductive Serum testosterone/estradiol- Blood

Follicle-stimulating hormone - BloodPulmonary Arterial oxygen saturation - Pulse oximeter

Peak expiratory flow - SpirometerBone Bone density - DXA scan

Bone turnover markers - Blood, fasting urineMuscle Skeletal muscle mass - DXA scan, body impedance

Grip strength - Dynamometer

Source: Wolfe B, Evans W,Seeman T. The biologicalconsequences of healthinequalities (2012). RusselSage Foundation, New York

SES and immune system biomarkers

Alley et al. Socioeconomic status and C-reactive protein levels in the US population: NHANES IV. Brain Behav Immun. 2006Sep;20(5):498-504

NHANES IV

Epigenetics – DNA methylation

Epigenetic modifications

Functionally relevant modifications to the genome that do notinvolve a change in the nucleotide sequence. Examples of suchmodifications are DNA methylation and histone modification,both of which serve to regulate gene expression without alteringthe underlying DNA sequence.

Gene expression

Phenotype

Dominance rank and expression level of pro-inflammatory 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 literaturereview: 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 acrossthe lifecourse

• Adjustment for potential confounding fromlifestyle factors

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Mean methylation difference (low vs high SES)

Lifecourse SES trajectories

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Mean methylation difference (low vs high SES)

Father's occupational position

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Mean methylation difference (low vs high SES)

Household's highest occupational position

A

B

C

Indicators of socioeconomic statusare associated with DNAmethylation of candidate genes.The graphs represent the plot ofbeta coefficients and p-values fromlinear regression of CpG sites onsocioeconomic indicators,adjusted for age, sex, season ofblood collection and diseasestatus. The red line represents thecorrected overall critical p-valueafter a multiple-test procedure(FDR). Data points on or above thered line correspond to rejected nullhypotheses (p-values thatremained significant after multiple-testing). For household’s highestoccupational position (B)26 datapoints are above the red line; forlifecourse socioeconomictrajectory (C), 7 data points.

Stringhini et al, InternationalJournal of Epidemiology 2015

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NFATC1 MAP3K6 GPR132 IL1A CXCL2

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Low

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Lifecourse SES trajectory

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Preliminary evidence: application of Horvath model of ageing(biological clock based on methylation) to EPIC-Italy data.

A «socio-molecular» study fromexisting cohorts:

LIFEPATH

Section Title

Enter text here

www.environment-health.ac.uk

Participant organisation name Country

Imperial College London - P Vineis (Coordinator), M Ezzati, P Elliott, M Chadeau-Hyam, ACVergnaud

UK

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

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 itis already experienced by individuals of high socio-economicstatus (SES).

To improve the understanding of the mechanisms through whichhealthy ageing pathways diverge by SES, by investigating life-course biological pathways using omic technologies.

To examine the consequences of the current economic recessionon health and the biology of ageing (and the consequent increasein social inequalities).

To provide updated, relevant and innovative evidence for healthyageing 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 andrepeat biological samples (total population >33,000).

3. Other large cohorts with biological samples (total population >202,000and a large registry dataset with over a million individuals with very richinformation on work trajectories and health.

4. A randomized experiment on conditional cash transfer for povertyreduction in New York City.

Data will be harmonized and integrated to conceptualize healthy ageing as acomposite outcome at different stages of life, resulting from life-courseenvironmental, behavioural and social determinants.

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

Late life

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

Markers already measured or whose measurement is funded/on-going, bygeographical location of the cohorts and life stage. IM=inflammation markers.

A new paradigm for the study of environmental causes ofdisease: the EXPOSOME

Relationships between macro-environment and micro-environment

S.M. Rappaport and M.T. Smith, Science, 2010: 330, 460-461

19

IARC: Exposome-Explorer

PBDEs

PAHs

PCDDs

PCBs

PCDFs

FATTY ACIDS

CAROTENOIDS

POLYPHENOLS

Pesticides

350 environmental

pollutants

147 dietarycompounds

- Chemistry- Cohorts where measured- Biospecimens- Analytical methods- Concentrations- State of validation- Correlations with exposures- Confunding factors- Available on-line- Linked to other databases

All biomarkers- 497 biomarkers- 10,480 concentration values

Biomarkers for environmentalpollutants- 350 biomarkers- 7,342 concentration values- 265 publications analyzed

365 concentrationvalues

Thank you