The Pregnancy Exposome: Multiple Environmental Exposures in the INMA-Sabadell Birth Cohort.

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Environmental Science & Technology

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The Pregnancy Exposome: multiple environmental exposures in the INMA-Sabadell Birth Cohort

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Oliver Robinson 1, 2, 3*, Xavier Basagaña1, 2, 3, Lydiane Agier 4, Montserrat de Castro1, 2, 3

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Carles Hernandez-Ferrer 1, 2, 3, Juan R. Gonzalez1, 2, 3, Joan O. Grimalt5, Mark

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Nieuwenhuijsen1, 2, 3, Jordi Sunyer1, 2, 3, Rémy Slama4 and Martine Vrijheid 1, 2, 3

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Affiliations: (1) Centre for Research in Environmental Epidemiology (CREAL),

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Barcelona, Spain; (2) Universitat Pompeu Fabra (UPF), Barcelona, Spain; (3) CIBER

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Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; (4) Team of

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Environmental Epidemiology applied to Reproduction and Respiratory Health, Inserm

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and Univ. Grenoble-Alpes, U823 (IAB) Joint Research Center, Grenoble, France; (5)

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Department of Environmental Chemistry, Institute of Environmental Assessment and

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Water Research, Barcelona, Spain

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*Corresponding author

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Parc Recerca Biomèdica de Barcelona

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Doctor Aiguader, 88

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08003 Barcelona

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Spain

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[email protected];

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Tel +34 932 14 7362

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1 ACS Paragon Plus Environment


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Abstract figure

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ABSTRACT

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The “exposome” is defined as ‘the totality of human environmental exposures from

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conception onwards, complementing the genome’ and its holistic approach may

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advance understanding of disease aetiology. We aimed to describe the correlation

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structure of the exposome during pregnancy to better understand the relationships 2 ACS Paragon Plus Environment

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between and within families of exposure and to develop analytical tools appropriate to

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exposome data. Estimates on 81 environmental exposures of current health concern

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were obtained for 728 women enrolled in The INMA (INfancia y Medio Ambiente)

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birth cohort, in Sabadell, Spain, using biomonitoring, geospatial modeling, remote

35

sensors and questionnaires. Pair-wise Pearson’s and polychoric correlations were

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calculated and principal components were derived. The median absolute correlation

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across all exposures was 0.06 (5th – 95th centiles: 0.01 – 0.54). There were strong

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levels of correlation within families of exposure (median = 0.45, 5th – 95th centiles:

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0.07 – 0.85). Nine exposures (11%) had a correlation higher than 0.5 with at least one

40

exposure outside their exposure family. Effectively all the variance in the dataset

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(99.5%) was explained by 40 principal components. Future exposome studies should

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interpret exposure effects in light of their correlations to other exposures. The weak to

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moderate correlation observed between exposure families will permit adjustment for

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confounding in future exposome studies.

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INTRODUCTION

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Environmental chemical and physical exposures during fetal or early life have been

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associated with adverse fetal growth and with developmental neurotoxic, immunotoxic

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and obesogenic effects in children, although for many of these associations evidence has

50

been classified as limited or inadequate

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pathologies and it is hypothesised that improved understanding of how environmental

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risk factors co-exist and interact during early life, can help elucidate their causes 5-7. It is

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clear that, up to now, the environment and child health field has almost uniquely

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focused on single exposure-health effect relationships; there is no global view of how

55

various types of exposures co-exist and jointly impact on health. The concept of the

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“exposome” 8 has attracted growing interest in recent years and is defined as the totality

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of human environmental (i.e. non-genetic) exposures from conception onwards,

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complementing the genome. It is hoped that through the use of holistic and data-driven

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approaches pioneered in the genomics fields, similar advances can be made in

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understanding the environmental component of disease aetiology. Since the developing

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fetus is particularly vulnerable to potential environmental hazards and since exposures

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during the critical in utero period may have a lifetime impact, the pregnancy period is

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an important starting point in characterizing the life course exposome9.

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Implementing the exposome concept however poses a number of challenges. Firstly,

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full measurement of the exposome at even a single time point is probably impossible.

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While ‘top-down’ measurement of exposome signals, either through measurement of

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the global internal biological response using molecular ‘omic technologies or the

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untargeted analysis of chemicals present in biological samples, addresses this issue to

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some extent, current analytical technology is not sensitive or flexible enough to capture

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and identify the components of the exposome in a single analytical sweep

1-4

. These are highly complex chronic


10, 11

.

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Therefore, a complementary approach is to construct the exposome from the ‘bottom-

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up’ using existing tools of exposure assessment such as bio-monitoring across various

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analytical platforms, geo-spatial modelling and questionnaires. Although this is a

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laborious process it should be recognised that even partial exposome coverage will be

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valuable and other ‘omic-wide scans such as the genome wide association studies

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(GWAS) rely on incomplete coverage, supplemented with imputation based on

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resources such as haplotype databases

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which provide sufficient sensitivity and specificity in the face of the high

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dimensionality and dense correlations inherent in exposome data, will be required for

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exposome-health association studies. Understanding what a typical exposome looks

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like, including the structure of correlations between and within groups of exposure is an

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important first step in planning the optimal use of both targeted exposome

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measurements and statistical analyses. Some of the first exposome studies

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used the cross-sectional National Health and Nutrition Examination Survey (NHANES)

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bio-monitoring data, which has provided reference values of contamination levels to a

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wide range of both environmental pollutants and nutrients

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recently described the correlation structure of the NHANES dataset, giving ranges of

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absolute correlation for each exposure group analysed, providing important information

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on part of the exposome structure of the general U.S. population. Furthermore they

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proposed that transparent knowledge of the correlation structure of a dataset is required

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to best interpret reported results using that dataset, which over the course of particular

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study may number hundreds of publications.

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INMA (INfancia y Medio Ambiente) is a birth cohort study in seven regions of Spain

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that aims to examine the role of environmental pollutants during pregnancy and early

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childhood in relation to child growth and development

12, 13

. Secondly, appropriate statistical tools,


16

14 15

have

. Patel and Ioannidis

18

17

. The INMA Sabadell


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subcohort, situated in Catalonia, has already described levels of exposure to a range of

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environmental factors during pregnancy including outdoor and indoor air pollution 19-22,

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persistent organic pollutants (POPs,

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substances (PFAS,

25

), metals

23

, brominated flame retardants

26-28

, phenols and phthalates

24

, perfluoroalkyl

29, 30

, disinfection by-

100

products in water 31, environmental tobacco smoke 32, insecticides

33

101

34

102

environmental exposure among pregnant Spanish women with the aim of better

103

understanding the correlation structure of an important part of the ‘pregnancy

104

exposome’.

and green spaces

. Here we present an analysis of relationships within and between important groups of

105

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METHODS

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A full description of the project protocol has been previously described

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during 2004 – 2006, pregnant women (N = 728) from the general population were

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recruited at the first trimester routine antenatal care visit in the main public hospital or

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health center of reference, using the following inclusion criteria: Women had to be at

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least 16 years old, intend to deliver in the reference hospital, have a singleton pregnancy

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with no assisted conception, and have no problems with communication. The study was

113

conducted with the approval of the hospital ethics committee, and written informed

114

consent was obtained from all women.

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Estimates on 81 exposures covering the pregnancy period, were collated into a single

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data set. Exposures were selected on the basis of availability from current or on-going

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INMA studies. Biomonitoring data included organochlorines (including pesticides and

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polychlorinated biphenyls (PCBs)) and PFAS in serum; mercury in cord blood;

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polybrominated diphenyl ethers (PBDEs) in breast milk; metals, phthalates, bisphenol A


18

. Briefly,

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in urine (averaged over measurements in samples collected during the first and third

121

trimesters) and cotinine in urine (from third trimester) (Table 1). Biomarker

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measurements where the analyte was non-detectable in over 85% of samples (including

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lead in cord blood, PCB congeners 28, 52, 101, dichlorodiphenyltrichloroethane (DDT)

124

and PBDE congeners 17, 28, 71, 66, 138 and 190) were excluded from the analysis.

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Geospatial modeling and remote sensing data included air pollutants (including nitrogen

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oxides, particulate matter (of diameter less than 2.5 µm (PM2.5), less than 10 µm (PM10)

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and between 2.5 µm and 10 µm (PMcoarse)) PM2.5 absorbance (a measure of black

128

carbon) and various elemental fractions of PM2.5 and PM10), the built environment

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(building density, street connectivity and green spaces), noise (averages over day,

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evening and night) and land surface temperature. Questionnaire data, collected by

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trained interviewers during the third trimester, included four home environment related

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binary variables including gas cooking, home and garden pesticide use, and

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environmental tobacco smoke exposure. Water use habits collected by questionnaire

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were combined with modeled levels of disinfection byproducts (total trihalomethanes,

135

brominated trihalomethanes and chloroform) in the residential water supply to calculate

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daily ingestion. References for the original studies and methods used are shown in table

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1. Additionally in this study, estimates on noise, surface temperate, building density and

138

street connectivity were assigned to the home address of participants within the ArcGIS

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platform (ESRI ® ArcMap TM 10.0, ArcGIS Desktop 10 Service Pack 4, spatialite

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v.4.11). Noise exposures were obtained from the strategic noise maps for Sabadell

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produced by the Generalitat de Catalunya under the European Noise Directive 35. Total

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building area and number of street intersections within 100m radius buffer were

143

calculated from topographical

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temperature was calculated from LANDSAT thermal imagery within a 50m radius

36

and road network maps

37


. The radiometric surface


145

buffer from the home address38. Exposures were grouped into families depending on

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their structure (for individually measured biomarkers) or source (other exposures)

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(Table 1).

148


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Table 1: Exposure families included in analysis.

Exposure family

Number of exposures measured

Number of women

PFAS

4

433

Matrix

Sampling time

Serum

1st trimester

Organochlorines

6

637

PBDEs

8

242

Colostrum

Metals

13

range: 243 -489

Urine and Cord blood (for mercury)

Phthalates

10

391 Urine

Birth Average of 1st and 3rd trimester spot urines and birth (for mercury) Average of 1st and 3rd trimester spot urines

Method

Reference

HPLC-MS

25

GC-MS

23

GC-MS

39

Q-ICP-MS, AAS (for Mercury)

26, 28

HPLC-MS

29

HPLC-MS

29 31

Bisphenol A

1

497

Water pollutants

3

561

Home address and questionnaire

Pregnancy average

Model and interview

Home environment

4

616

Questionnaire

3rd trimester

Interview

Cotinine

1

597

Urine

Air pollutants

24

range: 573 - 611 range: 477 -720

LC-MS Land use regression models Calculation from maps. Landsat imagery (Nomalised vegetation difference Index).

33

32

32

19, 20, 22

Built environment

3

Noise

3

631

Municipal Strategic noise maps

35

Surface temperature

1

728

Landsat imagery (thermal band)

38

Home Address

Pregnancy average

34, 36, 37

150

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Continuous variables were log-transformed to give a normal distribution. Biomarker

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measurements below the detection limit were imputed using distribution-based multiple

153

imputation 40. The proportion of biomarker measurements below the detection limit are

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shown in table S1 (supporting information). Pair-wise Pearson’s correlations (for

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continuous variables) and polychoric correlations (for correlations involving binary

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variables) between each individual exposures were calculated to produce a correlation

157

matrix. Heat map and circos plots were made to display the correlations. Principal


21


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components were then derived directly from the correlations. All analyses were

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conducted in R software environment (http://www.r-project.org/index.html).

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RESULTS

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The number of women with available exposure estimates ranged from 242 for the

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PBDEs to 728 women for temperature (mean number of women per exposure: 501). All

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exposures, along with summary statistics of their levels, are listed in the supplementary

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table s1. The percentage relative standard deviation (standard deviation / mean) for

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each exposure ranged from 3% for surface temperature at the home address to 531% for

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mono (4-methyl-7-hydroxyoctyl) phthalate (7OHMMeOP) (figure 1, table s1), with a

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mean relative standard deviation across all exposures of 84%. The mean correlation (r)

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across all exposures was 0.08, with standard deviation of 0.21 (median = 0.02; 5-95th

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centiles = -0.12 to 0.54). The mean absolute correlation was 0.13, with standard

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deviation of 0.18 and range 0.00 to 1.00 (median = 0.06; 5 - 95th centiles = 0.01 – 0.54).

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PFHxS PFOA PFOS PFNA DDE HCB bHCH PCB153 PCB180 PCB138 BDE47 BDE100 BDE99 BDE85 BDE154 BDE153 BDE183 BDE209 Hg Co Ni As Cu Zn Se Mo Cd Sb Cs Tl Pb MEHP MEHHP 5cxMEPP 2cxMMHP MEP MiBP MnBP 7OHMMeOP MBzP MEOHP BPA THM CHCl3 BTHM Cotinine Benzene PM25CU PM25FE PM25K PM25Ni PM25S PM25Sl PM25V PM25Zn PM10Cu PM10Fe PM10K PM10Ni PM10S PM10SI PM10V PM10Zn NO2 NO NOx PM25 PM10 PMcoarse AbsPM25 Dens Conn Green Noise_d Noise_e Noise_n Temp

0

100

200

300

400

500

600

Relative standard deviation (%) 172 173

Figure 1: Relative standard deviation (standard deviation / mean) for each continuous

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exposure. Abbreviations for all exposures are shown in table S1.

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There were strong levels of correlation within families of exposure with absolute

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correlations strongest among the noise indicators (median r= 0.99) and weakest among

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the home environment exposures (median r= 0.08) (figure 2). The water disinfection by-



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products and air pollutants had strong median levels of absolute correlation (r=0.67 and

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0.53, respectively) although with large ranges. The four PFOA compounds had the

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strongest median absolute correlation (r=0.62) of the individually measured biomarkers.

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The other biomarker families, PDBEs, phthalates, metals and organochlorines all had

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median absolute correlations below 0.5, reflecting their more diverse sources. However

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some pair-wise correlations within each of these families were above 0.5. The built

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environment measures showed lower levels of correlations between them, with an

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absolute median correlation of 0.16. The strongest correlation within the home

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environment exposures was between use of home and garden pesticides (r= 0.16).

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Overall, the median of all within-family absolute correlations was 0.45 (5th – 95th

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centiles, 0.07 – 0.85).


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0.6 0.4

(all)

Home Environment

Built Environment

Metals

Phthalates

Organochlorines

PBDEs

Air Pollutants

PFOAs

Water Pollutants

Noise

0.0

0.2

correlation

0.8

1.0

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Family

189 190 191 192 193 194 195 196

Figure 2. Pairwise correlations (absolute value) within families of exposure (for families with more than one exposure). Boxes illustrate interquartile range (IQR) with median displayed as a thick horizontal black line in the middle of the box. The whiskers extend to the most extreme data point, which is no more than 1.5 times the IQR from the box. Outliers are shown in actual points. ‘(all)’ denotes correlation across all pairs of variables available; the horizontal line on the graph denotes the 95th percentile of these absolute correlations.

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The correlation heatmap (figure 3) displays the linkage across all exposures by their

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correlation. ‘Blocks’ of high correlation within families of exposure were observed

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along the main diagonal of the heat map, with certain groups such as the

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organochlorines and phthalate metabolites showing less dense within-family

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correlations than more closely linked exposures such as the PFAS. With respect to

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between family correlations, no exposure had an absolute correlation higher than 0.6 13 ACS Paragon Plus Environment


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with an exposure outside its family. Nine exposures (11% of all 81 exposures) had an

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absolute correlation higher than 0.5 with at least one exposure outside its family. These

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included nighttime noise, which had a correlation of 0.52 with the air concentration of

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the copper fraction of PM2.5; proximity to green spaces, which had negative correlations

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with nitrogen oxides and PM2.5 absorbance; and building density which was positively

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correlated with benzene, nitrogen oxides and PM2.5 absorbance. 26 exposures (32%) had

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an absolute correlation higher than 0.4 with at least one exposure outside its family: In

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addition to further associations between noise variables, green space proximity, building

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density and air pollutants, we observed a positive correlation of 0.43 between street

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connectivity and the nickel fraction of PM10. Street connectivity also had a correlation

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of 0.39 with the nickel fraction of PM2.5 and a correlation of 0.32 with the vanadium

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fraction of PM2.5. Surface temperature had a correlation of 0.41 with the vanadium

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fraction o PM2.5 and correlations above 0.3 with 11 other air pollutants. As may be

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expected, urinary cotinine was correlated (r= 0.35) with self-reported environmental

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tobacco smoke exposure. In general for those exposures measured individually, through

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biomarker or questionnaire, there was low correlation between exposures in separate

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families: Only three pair-wise correlations between biomarker-measured exposures in

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separate families were above 0.3 with the strongest correlation observed between

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perfluorooctane sulfonic acid and PCB-153 (r = 0.32). Overall, the median of all

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between-family absolute correlations was 0.05 (5th – 95th centiles, 0.01 – 0.23).


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225 226 227

Figure 3: Correlation heatmap, showing pair correlations across all exposures, with blue colour indicating positive correlations and red colour indicating negative correlations. Abbreviations for all exposures are shown in table S1.

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229

Only three principal components were required to explain 50 % of variance across the

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whole data set, while six components explained 70% of variance and 22 components

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explained 95% of the variance (supplementary figure s1, table 2). The components were

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not solely loaded onto single exposure families. The exposures most strongly loading

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onto the first component (absolute loading > 0.10) were primarily outdoor environment

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exposures, including all the air pollutants, building density, noise, surface temperature

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(range of loadings: - 0.11 to -0.19) and green spaces (0.16). The second component was

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composed primarily of positive loadings to the PFAS (0.11 to 0.15), PBDEs (0.12 to -

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0.22), hexachlorobenzene (0.14), PCB congeners 153 and 180 (both 0.11), and some

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metals (0.11 to 0.17) and negative loadings to the phthalates (Monoethyl phthalate

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(MEP) = -0.08 and others -0.13 to -0.24) and BPA (-0.15), with further contributions

240

from cobalt (-0.11) and home pesticides (-0.17). The third component was composed of

241

positive loadings to all the metals except mercury and cobalt (0.18 to 0.27) and strong

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negative loadings to the PFAS, the organochlorines (except DDE), five of the PBDEs

243

and three of the phthalates (-0.10 to -0.18). 99.5% of variance in the dataset, which may

244

be considered effectively all variance, was explained by 40 components. Within each

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exposure family, only one component was needed to explain the 99.5% of variance

246

among the three noise variables, 10 components were needed to explain 99.5% of

247

variance among the 24 air pollution variables while 12 components were needed to

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explain 99.5% of variance among the 14 home environment variables (table 2).

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Principal component loadings can be found in supplementary table s2.

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Table 2. Principal component (PC) analysis showing the numbers of components required to explain percentages of cumulative variance by each exposure group and across all exposures. Number of PCs required to explain % of cumulative variance: 50 70 95 99.5

Exposure Group

Number of variables

PFAS Organochlorines PBDEs Metals Phthalates Bisphenol A Water Pollutants Cotinine Home Environment Air Pollutants Built environment Noise Temperature

4 6 8 13 10 1 3 1 4 24 3 3 1

2 1 2 2 1 1 2 2 1 2 -

2 2 3 4 3 1 2 2 2 1 -

3 3 5 9 5 2 3 5 2 1 -

3 4 6 11 7 2 3 10 2 1 -

All

81

3

6

22

40

254 255



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DISCUSSION

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Since its initiation in 2004, the INMA Sabadell birth cohort has measured exposure to

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many of the most important environmental factors of current concern to child health,

259

providing a wide range of exposure estimates covering the in utero period for a

260

substantial number of Spanish women. This has provided a rich resource of

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environmental data for the study of longitudinal health outcomes in children, and has

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now allowed a first picture of the structure of an important piece of the pregnancy

263

exposome, considered a key period in constructing a life course exposome.

264

The presented correlation structure enables improved interpretation of results reported

265

by both the INMA Sabadell birth cohort and in epidemiological studies in general. As

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with other reported exposure correlations

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within families of exposure (grouped by structure or source) and therefore results

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reported for single exposures need to be interpreted in light of their correlations to other

269

exposures within their respective families. An increasing number of studies are now

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including multiple within family exposures

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pollution studies should consider multiple pollutants simultaneously, although in the

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presence of high correlation it becomes difficult to disentangle the effects of each

273

pollutant

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exposures measured in individual women and with other families of exposure. This

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provides confidence that reported results for biomarker exposure estimates are not

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confounded by correlation with other unreported exposures and provides scope for

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epidemiological studies to separate the effects of each exposure group. We do however

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see stronger levels of correlation between families of exposures encountered in the

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outdoor environment such as air pollutants, noise, temperature and the built

280

environment indicating that studies focusing on one of these families should be

17, 41

9

we find strong levels of correlations

and it has long been recognized that air

42

. We see weak levels of correlation between the families of chemical


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interpreted with caution. However, the range of between-family correlations found in

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this study, all lower than 0.6, would allow disentangling of their effects if all exposures

283

have been measured. Future studies should consider these families of outdoor exposures

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in combination in order to provide appropriate risk estimates, an approach adopted now

285

by a growing number of studies 43, 44.

286

In the environment-wide association study (EWAS) approach to exposome analysis,

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analogous to GWAS, adopted by Patel and colleagues

288

implicitly by research projects with repeated publications), multiple analyses of single

289

pollutants must be adjusted to guard against generation of false positives by a

290

Bonferroni correction or similar. In the presence of correlations between exposures (or

291

linkage disequilibrium between single nucleotide polymorphisms (SNPs) in GWAS),

292

Bonferroni correction would be overly conservative and instead correction should be

293

made based on effective, rather than actual variables. Patel and Ioannidis

294

the 530 exposure variables available in the NHANES dataset could be reduced to 476

295

‘effective variables’ based on the within family correlations following the method of

296

Nyolt

297

required to explain the variances observed for each exposure group and across exposure

298

groups for the whole data set. Following the method of Gao et al.46, which was

299

demonstrated to provide more efficient multiple testing correction when there is high

300

linkage disequilibrium (or correlation in the exposome context), the number of effective

301

variables is equivalent to the number of principal components required to explain 99.5%

302

of the variance. Therefore of the 81 variables analysed here, we find that there are 40

303

‘effective variables’ that explain practically all the variance contained in the dataset. A

304

hypothetical EWAS analysis of the INMA Sabadell pregnancy exposome dataset using

14

(and they argue, adopted

17

report that

45

. Here for simplicity we have presented the number of principal components



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a Bonferroni-type correction may therefore choose a p value threshold of 0.001 (i.e.

306

0.05/40).

307

Although the EWAS approach is flexible, other methodologies (reviewed in47) may

308

prove more appropriate. The presented results may be used as a foundation in

309

simulation studies to assess the performance of different statistical models for the

310

analysis of exposome data. However one difficulty when analysing associations with

311

health outcomes using this dataset, particularly when applying multivariate methods, is

312

missing values. To maintain a breadth of exposures that approaches an ‘exposome’

313

dataset, while also retaining a sufficient number of observations would not be possible,

314

since not all exposures, particularly those derived from biomarkers, were available for

315

all women. A common solution in these situations is to use an imputed dataset

316

which is a justifiable approach for analyses on large populations, providing certain

317

assumptions hold 49. Imputation also provides a more general solution to providing the

318

wide coverage of the external exposome required for agnostic exposome wide scans.

319

Since we find that effectively all the variance in this dataset could be explained by much

320

fewer principal components than the actual number of variables measured, improved

321

knowledge of the correlation structure of the exposome may allow external exposome

322

assessment based on fewer measured exposures. To return to the GWAS analogy, wide

323

and more cost effective genome coverage is provided by genotyping of a few hundred

324

thousand SNPs, which provide information on several million base pairs based on

325

imputation. ‘Hits’ detected in imputed regions may then be followed up with deep

326

sequencing to confirm results. The required correlation or haplotype information is

327

provided by consortia who have conducted full genome sequencing on only a relatively

328

small number of individuals representing a range of different ethnicities 12, 13. Although

329

we have presented PCA to describe the underlying dimensions of the data, it may not be 20 ACS Paragon Plus Environment

41 48

,

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the optimal strategy to select the exposures best able to provide a wide coverage of the

331

exposome. Several other variable reduction techniques exist

332

selected variables may vary according to each technique.

333

Whether this approach is applicable to exposome research will depend on how

334

reproducible the exposome correlation structure is across spatially and temporally

335

distributed populations. Only some of the correlations reported here may be compared

336

to correlations reported in other datasets such as NHANES 17 and the study of Lenters et

337

al.

338

Greenland, Ukraine and Poland. For instance of the 13 phthalate metabolites measured

339

in NHANES the median absolute correlation is 0.25, of the four phthalate metabolites

340

reported by Lenters et al. the median absolute correlation is 0.38, and the median

341

absolute correlation of the 10 phthalate metabolites measured here is 0.30. The

342

correlation levels will however, be very dependent on the analytes chosen within

343

particular families; it is of interest that the median absolute correlation of four PFAS

344

that were measured both in this study and in the study of Lenters et al. was very similar

345

(0.62 and 0.68 respectively). Between-family correlations were generally weak in both

346

the study of Lenters et al. and as reported here. However Lenters et al. found relatively

347

strong correlations between mercury, the PFAS and PCB-153, which were absent

348

among the INMA Sabadell women. This is likely explained by the higher levels found

349

for all these chemicals among the studied Greenland population since correlations were

350

not reported separately for the three included regions. Thus while some aspects of the

351

correlation structure of exposomes of populations around the world are similar, other

352

aspects will depend on the particular environment and lifestyles of the population

353

studied, such as the consumption of large marine animals in the Greenland population.

354

A future ‘Human Exposome Project’ will need to consider measurements in a range of

41

50

, and the final set of

that measured multiple biomarkers among males of reproductive age living in



355

populations with standardized exposure measurements (with respect to analyte, method

356

and matrix analysed).

357

The INMA Sabadell pregnancy exposome dataset provides broad coverage of the

358

exposome since indicators of most environmental exposure groups of key current

359

concern are included, covering biomarker measurements of both persistent and non-

360

persistent pollutants, questionnaire information on personal commercial product use,

361

and geospatial modeled estimates on air and water contaminants. Exposures derived

362

from geospatial models are not included in the NHANES dataset but should be included

363

in characterisation of the external exposome since they provide information for many

364

exposures for which specific biomarkers are not available. However, we observed

365

overall differences in the variability between those exposures measured through

366

biomarkers and those exposures that were assigned based on address. This may be

367

problematic for exposome analyses since statistical models may have reduced

368

sensitivity to detect health effects from those exposures with lower variability.

369

Biomarker measurements may have higher between subject variability because they

370

incorporate information regarding both prevalence in the environment and personal

371

behaviour. The precision of the geospatially derived estimates will be improved when

372

supplemented with information about how individuals move through their environment,

373

now becoming available from smartphones51. Similarly the binary estimates on personal

374

product use would be improved with the use of more detailed questionnaires. Further

375

limitations to the current analysis include pre-selection of the included analytes which

376

may limit their utility in truly agnostic exposome analyses 11. Future measurements of

377

the external exposome may consider choosing indicator exposures to provide the widest

378

exposome coverage (i.e. representative of most exposure groups) rather than those of

379

most regulatory concern. Furthermore one must consider that parts of the correlation 22 ACS Paragon Plus Environment

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380

structure presented here are composed of analytical variability; those exposures

381

measured using the same analytical platform may show greater within platform

382

correlation compared to those measured on other platforms, obscuring ‘true’ biological

383

variability and correlation. Outdoor exposure models constructed from the same

384

variables, such as traffic density, may similarly show inflated correlations. A final

385

important limitation is the different degrees of exposure misclassification between

386

exposures. As with all exposure assessment, efforts are needed to improve assessment

387

of each exposure. Misclassification may be high for non-persistent exposures such as

388

BPA since it is known that within person variability for these compounds is high

389

Despite addressing this to some extent using the average of urinary measurements at

390

two time points, exposure misclassification will be greater than for other exposures such

391

as air pollution for which routine monitoring can provide daily and relatively accurate

392

exposure estimates at the address level

393

differential measurement error may decrease the accuracy of joint effect estimates, with

394

the effect of the well-measured exposures dominating the effect estimates for correlated

395

but less well-measured exposures 53.

396

These limitations may only be overcome with the development of an ‘exposome chip’

397

for a single exposome analysis or similarly, the concurrent analysis of the ‘top-down’

398

exposome and its relationship to the ‘bottom-up’ exposome presented here

399

Potential future analyses in the INMA Sabadell exposome dataset involve the inclusion

400

of other parts of the external exposome such as diet, physical activity and drug use and

401

more general social and economic factors. This more complete external exposome could

402

also be examined in relation to available measures of the internal exposome (i.e the

403

biological response and endogenously derived exposures) that include metabolome,

404

DNA methylation and inflammatory markers. Assessment of the internal exposome

52

.

20

. Combined analyses of exposures with


11, 54

.


405

using molecular 'omic technologies may allow more appropriate grouping of external

406

exposures based on shared toxicogenomic effects.

407

wide research projects such as the HELIX project

408

large numbers of subjects of both the external and internal exposomes and test the

409

utility of both the ‘bottom-up’ and the ‘top-down’ approaches.

410

In summary, the correlation analysis presented here of multiple environmental

411

exposures among pregnant women, provides a first picture of the structure of the

412

exposome during the crucial in utero period. This information will aid interpretation of

413

reported findings from epidemiological studies in general and inform future analyses of

414

the exposome.

415

ACKNOWLEDGEMENTS

416

We would like to thank all INMA researchers on whose previous work this study is

417

based. We thank all the INMA study participants and the members of the HELIX-

418

EXPOsOMICs statistical working group who provided helpful discussions

419

This research received funding from the European Community’s Seventh Framework

420

Programme (FP7/2007-2013) under grant agreement 308333– the HELIX project.

421

The INMA Sabadell cohort was funded by grants from Instituto de Salud Carlos III

422

(Red INMA G03/176, CB06/02/0041), Spanish Ministry of Health (FIS-PI041436, FIS-

423

PI081151), Generalitat de Catalunya (CIRIT 1999SGR 00241), Fondo de Investigacion

424

Sanitaria (ISCIII: FIS-PI12/01890), Fundació La Marató De TV3(090430) and

425

Recercaixa (2010ACUP 00349).

426

We acknowledge the following studies, funded by the European Community’s Sixth

427

and Seventh Framework Programmes, for generating exposure estimates used in this

Furthermore, on-going European55

will provide broad coverage in a


Page 24 of 30

Page 25 of 30


428

analysis: The ESCAPE study (grant 211250), provided estimates on exposure to air

429

pollutants; the HIWATE study (Contract No. Food-CT-2006-036224), provided

430

estimates on exposure to water disinfection by-products; and the PHENOTYPE study

431

(grant 282996) provided estimates on green spaces.

432

SUPPORTING INFORMATION AVAILABLE

433 434 435 436 437 438

Supporting information includes: the full correlation matrix in excel format; summary descriptions of exposure levels and acronyms for all exposures; figure of cumulative variance explained by principal component analysis across whole dataset; exposure loadings for up to the first ten components for principal component analysis across whole dataset and for each exposure family. This information is available free of charge via the Internet at http://pubs.acs.org.



439

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10 rse PM coa 25 PM sPM Ab s n De nn Co en Gre e_d Nois _e Noise e is No _n Temp

C l3 H C BTH M Cot inin e Ga s ET S H_ G pe s _ pe ticid es s ze ticid es ne

Be n

BPA THM Pb HP P E M HH P EP ME xM HP 5c MM 2cx P ME P MiB MnBP eOP 7OHMM MBzP MEOHP

Cu Zn Se Mo Cd Sb Cs

7 E4 00 BD E1 BD E99 BD 85 E BD 54 E1 BD 3 E15 BD 183 BDE 09 BDE2 Hg Co Ni As

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PFHxS PFOA PFOS PFN A DDE HC B bH C PC H PC B15 3 PC B18 0 B1 38

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0.5 > r >= 0.3

Page 30 of 30

PM10Fe PM10K PM10Ni PM10 S PM1 0SI PM 10V PM 10Z NO n NO 2 NO PM x 25

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−0.3

Birth Outcomes in a Prospective Pregnancy-Birth Cohort Study of Environmental Risk Factors in Kuwait: The TRACER Study.

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