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RESEARCH ARTICLE
Long-Term Exposure to Ambient Air Pollution
and Metabolic Syndrome in Adults
Ikenna C. Eze
1,2
, Emmanuel Schaffner
1,2
, Maria Foraster
1,2
, Medea Imboden
1,2
,
Arnold von Eckardstein
3
, Margaret W. Gerbase
4
, Thomas Rothe
5
, Thierry Rochat
4
,
Nino Künzli
1,2
, Christian Schindler
1,2
, Nicole Probst-Hensch
1,2
*
1 Swiss Tropical and Public Health Institute, Basel, Switzerland, 2 University of Basel, Basel, Switzerland,
3 Institute of Clinical Chemistry, University Hospital Zurich, Zurich, Switzerland, 4 Division of Pneumology,
Geneva University Hospital, Geneva, Switzerland, 5 Zürcher Höhenklinik, Davos, Switzerland
* Nicole.Probst@unibas.ch
Abstract
Air pollutants (AP) play a role in subclinical inflammation, and are associated with cardio-
vascular morbidity and mortality. Metabolic syndrome (MetS) is inflammatory and precedes
cardiovascular morbidity and type 2 diabetes. Thus, a positive association between AP and
MetS may be hypothesized. We explored this association, (taking into account, pathway-
specific MetS definitions), and its potent ial modifiers in Swiss adults. We studied 3769 par-
ticipants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults,
reporting at least four-hour fasting tim e before venepuncture. AP exposures were 10-year
mean residential PM
10
(particulate matter <10μm in diameter) and NO
2
(nitrogen dioxide).
Outcomes included MetS defined by World Health Organization (MetS-W), International Di-
abetes Federation (MetS-I) and Adult Treatment Panel-III (MetS-A) using four- and eight-
hour fasting time limits. We also explored associations with individual components of MetS.
We applied mixed logistic regression models to explore these associations. The prevalence
of MetS-W, MetS-I and MetS-A were 10%, 22% and 18% respectively. Odds of MetS-W ,
MetS-I and MetS-A increased by 72% (51-102%), 31% (11-54%) and 18% (4-34%) per
10μg/m
3
increase in 10-year mean PM
10
. We observed weaker associations with NO
2
. As-
sociations were stronger among physically-active, ever-smokers and non-diabetic partici-
pants especially with PM
10
(p<0.05). Associations remained robust across various
sensitivity analyses including ten imputations of missing observations and exclusion of dia-
betes cases. The observed associations between AP exposure and MetS were sensitive to
MetS definitions. Regarding the MetS components, we observed strongest associations
with impaired fasting glycemia, and positive but weaker associations with hypertension and
waist-circumference-based obesity. Cardio-metabolic effects of AP may be majorly driven
by impairment of glucose homeostasis, and to a less-strong extent, visceral adiposity. Well-
designed pros pective studies are needed to confirm these findings.
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 1/19
a11111
OPEN ACCESS
Citation: Eze IC, Schaffner E, Foraster M, Imboden
M, von Eckardstein A, Gerbase MW, et al. (2015)
Long-Term Exposure to Ambient Air Pollution and
Metabolic Syndrome in Adults. PLoS ONE 10(6):
e0130337. doi:10.1371/journal.pone.0130337
Academic Editor: Stephania Ann Cormier,
University ofTennessee Health Science Center,
UNITED STATES
Received: January 6, 2015
Accepted: May 19, 2015
Published: June 23, 2015
Copyright: © 2015 Eze et al. This is an open access
article distributed under the terms of the Creative
Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: The analytical data set
and the statistical code are available from the
corresponding author upon request, since ethics
approval and participants’ consent do not allow public
sharing of data.
Funding: This study was supported by the Swiss
National Science Foundation [grants no. 33CSCO-
134276/1, 33CSCO-108796, 3247BO-104283,
3247BO-104288, 3247BO-104284, 3247-065896,
3100-059302, 3200-052720, 3200-042532, 4026-
028099, PMPDP3_129021/1, PMPDP3_141671/1];
the Federal Office for Environment; the Federal Office
Introduction
Metabolic syndrome (MetS) represents a group of symptoms including central obesity, hyper-
tension, atherogenic dyslipidaemias and insulin resistance. World Health Organization
(WHO) defines MetS (MetS-W) as diagnosis of impaired fasting glycaemia (IFG; or treatment
for type 2 diabetes) and of any two out of central obesity, hypertension, hypertriglyceridemia
(HTG) and low high-density lipoproteins (HDL) (or treatment for specific dyslipidaemia), and
urinary albumin excretion ratio 20μg/min [1]. International Diabetes Federation (IDF) de-
fines MetS (MetS-I) as central obesity and any two out of IFG, hypertension, HTG and low
HDL [2], whereas Adult Treatment Panel (ATP) III defines MetS (MetS-A) as diagnosis of any
three of five major components [3, 4]. MetS greatly contributes to global disease burden, occur-
ring in about 25% of adults [2]. It predisposes to cardiovascular events and type 2 diabetes.
Similarly, air pollutants (AP) are common, top risk factors for disease burden [5] and have
been associated with cardiovascular [6–8]-and diabetes-related events [9–11]. Controlling dis-
ease burden from cardiovascular morbidity and diabetes implies that prevention of MetS and
excessive AP exposure are crucial. Identifying modifiable risk factors to MetS will improve at-
tribution of the burden and support public health control strategies.
MetS enhanced susceptibility to adverse effects of short-term AP exposure. Experimental
exposure to diesel exhaust resulted in more haemoconcentration and thrombocytosis in MetS
subjects compared to healthy ones [12]. MetS subjects also developed cardiovascular symptoms
when exposed to ultrafine particles [13]. Susceptibility to low grade systemic inflammation on
exposure to long- term particulate matter <10μm (PM
10
) was enhanced by MetS [14]. Thus, a
link between AP exposure and MetS is plausible but has not been studied. Previous MetS-
related studies have focused on PM effects. Unlike PM, which is a marker of general pollution
and particle exposure, Nitrogen dioxide (NO
2
) is more specific for traffic-related pollution.
Studying NO
2
will reveal if traffic exposure contributes to the association, or whether the ob-
served association solely reflects a particle effect (pointing towards an innate immunity activa-
tion pathway) or a contribution of different sources. Studying the various definitions of MetS
will not only assess the sensitivity of associations to definition, but will also aid the understand-
ing of pathways most likely driving the cardio-metabolic effects of AP on a population level.
We therefore explored associations between long-term AP exposure and MetS in adults from a
general population sample.
Materials and Methods
Ethics Statement
Ethical clearance for the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in
Adults (SAPALDIA) was obtained from the Swiss Academy of Medical Sciences, the National
Ethics Committee for Clinical Research (UREK, Project Approval Number 123/00) and the
Cantonal Ethics Committees of the eight health examination areas (Aargau, Basel, Geneva,
Grisons, Ticino, Valais, Vaud and Zurich). Participants were required to give written consent
prior to the conduct of any health examination.
We used data from 3769 follow-up participants of the SAPALDIA study aged 29–73 years.
Details of this study are explained elsewhere [15] but briefly, SAPALDIA began in 1991 with
9651 participants randomly drawn from eight Swiss communities representing a wide range of
environmental conditions in Switzerland. 8047 individuals participated in the follow-up study
in 2001/2002. Participants completed computer-assisted interviews on health and lifestyle, and
had physical examinations including blood sampling, at follow-up, into a bio bank for bio-
marker and genetic assays. Inclusion in the present study required participation in the follow-
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 2/19
of Public health; the Federal office of Roads and
Transport; the cantonal governments of Aargau,
Basel-Stadt, Basel-Land, Geneva, Luzern, Ticino and
Zurich; the Swiss Lung League and the Lung
Leagues of Basel-Stadt/Basel-Landschaft, Geneva,
Ticino and Zurich. The funders had no role in study
design, data collection and analysis, decision to
publish, or prepare the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
up study, complete data on outcomes and covariates and at least four-hour fasting time before
the follow-up examination. The reduction in sample size for this study is primarily explained
by the exclusion of non-fasting subjects. Fasting status was not required for
SAPALDIA participation.
Definition of MetS
Participants reported their fasting time at first follow-up physical examination (including vene-
puncture). Height, weight, blood pressure (BP), plasma glucose and lipids were measured.
Blood pressure was measured twice at rest, on the left arm, at least three minutes apart, in a sit-
ting position. The mean value of both measures was computed for analyses. Participants were
asked about physician diagnoses of diabetes, hypertension, dyslipidaemia and use of medica-
tion for these conditions. We defined hypertension as BP (mmHg) 140/90 (MetS-W) and
>130/85 (MetS-I; MetS-A) or a physician diagnosis/ treatment. We defined low HDL as plas-
ma HDL (mmol/l) <0.9 (MetS-W) and <1.03 (MetS-I; MetS-A) in males and <1.0 and <1.30
respectively in females and/or diagnosis/ treatment of dyslipidaemias. We defined HTG as
plasma triglyceride (mmol/l) 1.7 and/or diagnosis/ treatment of dyslipidaemias, and im-
paired fasting glycae mia (IFG) as plasma glucose 6.1mmol/l (MetS-W) and 5.6mmol/l
(MetS-I; MetS-A) and/or diagnosis/treatment of diabetes. Waist circumference (WC) was not
measured at this visit, but was measured at the next follow-up visit. We derived a prediction
model, with optimal Bayesian Information Criterion, for waist circumference measured at the
next follow-up:
Waist circumference ðcmÞ¼b
0
þ b
1
sexþb
2
age þ b
3
age
2
þ b
4
BMI
þ b
5
BMI
2
þ b
6
age bmi þ b
7
sex age þ b
8
sex age
2
þ b
9
sex BMI þ b
10
sex BMI
2
þ b11 sex age BMI
þ b
12
alcohol þ b
13
physical activity þ b
14
ex
‒
smoker
þ b
15
current smoker:
We applied this model, using the covariate values of the second survey and added the residuals
from the third survey, to back-predict waist circumference for present analyses. We used cross-
validation to assess our imputation model, randomly splitting the follow-up sample into a
training and a validation sample. The mean imputation error was not significantly different
from zero, and the correlations of the imputation errors and the independent variables were
also not significantly different from zero. The adjusted R
2
of the imputation model was 0.79
and the squared correlation between the imputed and the actual values was of the same size.
We thus defined central obesity (MetS-I) for a European population as WC 94cm and
80cm for males and females respectively. We also defined central obesity (MetS-A) as WC
102cm and 88cm for males and females respectively. Central obesity can be assumed if
BMI>30 kg/m
2
[2]. Finally, we defined MetS-W, MetS-I and MetS-A based on the above criteria.
Assignment of exposures
We considered estimates of residential exposure to PM
10
and NO
2
. Annual means of AP for
1990 and 2000 were estimated from dispersion models using various emission inventories in-
cluding road and rail traffic, residential, agricultural, heavy equipment and industrial emissions
[16] on a 200x200m grid, and linked to participants’ addresses.[17] Estimates of NO
2
exposure
were obtained from a hybrid model inc orporating land-use regression, since the dispersion
model alone did not optimally predict NO
2
near traffic sites.[18 ] Annual data of AP at
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 3/19
monitoring sites and participants’ residential histories were used to estimate annual means of
residential exposure levels during the follow-up period and to assign estimates of average resi-
dential exposure over the 12 month and 10 year period, respectively, preceding the follow-up
examination.[17]
Potential confounders
Consistent with our previous report on diabetes [10], we considered the following characteris-
tics, measured at follow-up, as potential confounders: age, sex, educational attainment (9, >9
years), smoking status (never, former, current) and pack-years, passive smoke exposure (yes/
no), occupational exposure to vapours, gases, dusts or fumes (VGDF; yes/no), alcohol consump-
tion (including beers, wines, liquors and spirits) (never, once a day, > once a day), consump-
tion of raw vegetables (including salads, juices), citrus fruits (including juices) and other fruits
(including juices) (never, 3 days per week, >3 days per week respectively), and self-reported
vigorous physical activity defined as participation in activities making one sweat or breathless
(<0.5 and 0.5 hours/week). We also considered neighbourhood-level socio-economic index
(SEI) of participants, derived from a principal component analysis using median rent, number
of residents of households, educational level and occupation of household heads [19].
Statistical Analyses
We summarized participants’ characteristics by different MetS definitions and also by inclu-
sion/exclusion status. We estimated the prevalence of MetS-W, MetS-I and MetS-A, and their
associations with 10-year-means of exposure metrics, using mixed logistic models with a ran-
dom intercept for study area. Since metabolic syndrome is common [2] and given the preva-
lence in our study sample, we applied mixed Poisson models to estimate incidence rate ratios
and used a heuristic approach to obtain robust confidence intervals [20]. Our fully-adjusted
model included participants’ age, sex, educational attainment, neighbourhood SEI, smoking
status and pack-years, passive smoke and VGDF exposure, consumption of alcohol, vegetables,
citrus fruits and other fruits, and physical activity and BMI. We adjusted for continuous BMI
to capture its variation within obesity and non-obesity groups. Using this fully-adjusted model,
we also explored independent associations of PM
10
and NO
2
with MetS in two- pollutant mod-
els. We also explored associations between AP and components of MetS. All these models addi-
tionally included BMI except for the AP- obesity model. We repeated these analyses among
participants reporting at least eight-hour fasting time (N = 367).
We assessed potential effect modification by age (50, >50 years), sex, and physical activity,
diabetes and smoking status by stratification and interaction, given previously reports on their
role as potential modifiers of AP and diabetes association [21]. Sensitivity analyses included:
imputation of 75 observations (10 imputations) with missing data using chained equations; ex-
cluding participants who had IFG or obesity but not identified as MetS; treating study area as
fixed factor; omitting study area from the models. We applied inverse probability weighting
(IPW) to explore non-participation bias. We define d alternative MetS including MetS-I with
BMI-based central obesity and MetS-I with North American cut-offs for waist circumference.
We performed all analyses with STATA version 13 (Stata Corporation, Texas).
Results
Characteristics of participants
Table 1 shows the characteristics of included participants by MetS status. The distribution of
established risk factors with MetS generally followed expectations (e.g. male sex, smoking,
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 4/19
Table 1. Background Characteristics of participants.
Characteristic (%) MetS-W
a
MetS-I
b
MetS-A
c
No MetS
d
N 382 771 663 2617
Females 40.1 46.0 40.8 58.0
Education >9 years 85.1 88.9 88.5 93.6
Never smokers 37.2 43.3 44.6 45.0
ETS exposure 49.5 46.3 46.4 46.7
Occupational exposure to VGDF 45.0 45.2 45.1 42.4
Alcohol intake: None 13.1 9.9 9.9 9.9
once/day 72.2 76.4 75.3 81.7
> once/day 14.7 13.7 14.8 8.4
Citrus fruits intake: None 12.8 9.5 8.7 7.6
3days/week 54.2 54.5 55.7 56.8
>3days/week 33.0 36.0 35.6 35.6
Fruit intake: None 2.1 2.1 2.1 2.1
3days/week 26.4 30.2 30.8 33.7
>3days/week 71.5 67.7 67.1 64.2
Raw vegetables intake: None 0 1.0 0.7 0.7
3days/week 20.7 18.0 18.6 18.5
>3days/week 79.3 81.0 80.7 80.8
Vigorous physical activity 0.5hours/week 42.7 53.0 52.8 60.1
Impaired fasting glycaemia (IFG)
e
100 56.3 67.8 7.9/20.7
h
Low high-density lipoproteins (HDL)
f
41.6 51.1 65.6 6.9/14.7
h
High triglycerides 91.6 83.4 89.4 34.3
Obesity (BMI>30kg/m
2
) 49.0 36.4 34.0 9.3
Hypertension
g
81.9 82.4 82.0 25.5/36.3
h
Area:
Basel 13.4 11.3 10.0 10.6
Wald 14.6 13.7 16.5 16.1
Davos 2.6 8.6 8.2 9.1
Lugano 25.1 17.6 19.8 17.3
Montana 5.2 10.1 10.5 11.6
Payerne 14.7 15.2 12.3 11.9
Aarau 16.0 14.5 13.6 13.6
Geneva 8.4 8.9 9.1 9.9
Mean (SD)
Age (years) 61.4(7.3) 58.1 (9.1) 57.9 (9.2) 51.2 (11.5)
BMI (kg/m
2
) 30.3(4.9) 29.1(3.9) 28.7 (4.0) 24.8 (3.9)
Predicted waist circumference (cm) 100.7 (11.9) 100.3 (10.6) 98.8 (11.7) 83.5 (11.4)
Neighborhood SEI 61.7(10.3) 62.5(9.9) 62.9 (9.5) 63.2 (10.0)
Pack-years of cigarettes smoked 15.9(24.7) 13.4(22.4) 13.6 (22.2) 9.8 (16.6)
10-year PM
10
(μg/m
3
) 25.0(7.4) 22.7(7.9) 22.8 (8.1) 22.2 (7.8)
(Continued)
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 5/19
physical inactivity were more prevalent in MetS). The MetS cases also had higher exposures to
AP than the controls (Table 1).
MetS-W had a weakly positive correlation with MetS-I (kappa = 0.25), but both correlated
better with the MetS-A (kappa = 0.40 and 0.67 respectively). Differences between included and
excluded participants are shown in S1 Table. Included participants tended to be older, more ed-
ucated, never-smokers, more exposed to occupational dusts and less physically active (S1
Table).
Table 1. (Continued)
Characteristic (%) MetS-W
a
MetS-I
b
MetS-A
c
No MetS
d
10-year NO
2
(μg/m
3
) 29.9(11.4) 27.6(11.6) 27.5 (11.8) 27.2 (11.3)
MetS-W: World Health Organization-defined metabolic syndrome. MetS-I: International Diabetes Federation-defined metabolic syndrome. MetS-A: Adult
Treatment Panel III-defined metabolic syndrome. ETS: environmental tobacco smoke. VGDF: vapours, gases, dusts or fumes. SEI: socio-economic index
expressed as a percentage. PM
10
: particulate matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide.
a
defined as IFG and any two of central obesity, hypertension, low HDL and high triglycerides.
b
defined as central obesity and any two of IFG, hypertension, low HDL and high triglycerides.
c
defined as any three of IFG, central obesity, hypertension, low HDL and high triglycerides.
d
defined as not having a, b and c.
e
defined by WHO as fasting blood glucose6.1mmol/L and/or diagnosis of type2diabetes; and by IDF and ATP-III as fasting blood glucose5.6mmol/L
and/or diagnosis of type2diabetes. High triglycerides defined as fasting triglycerides1.7mmol/L or treatment for this condition.
f
defined by WHO as 0.9 mmol/L (males), 1.0 mmol/L (females); and by IDF and ATP-III as < 1.03 mmol/L (males), < 1.29 mmol/L (females), or
treatment for this condition.
g
defined by WHO as 140/90, or treatment of previously diagnosed hypertension; and by IDF and ATP-III as blood pressure >130/85 mm Hg or
previously diagnosed hypertension.
h
proportion in controls according to MetS-W/ MetS-I or MetS-A criteria respectively.
doi:10.1371/journal.pone.0130337.t001
Table 2. Association between air pollutants and metabolic syndrome (4-hour fasting time).
Model 10-year mean PM
10
P-Value 10-year mean NO
2
P-value
OR (95%CI) OR (95%CI)
MetS-W Model 1 1.64 (1.35, 1.98) <0.001 1.20 (1.02, 1.41) 0.025
c
Cases = 382 Model 2 1.58 (1.29, 1.95) <0.001 1.21 (1.02, 1.43) 0.026
c
Model 3 1.72 (1.46, 2.02) <0.001 1.22 (1.02, 1.46) 0.033
c
MetS-I
a
Model 1 1.23 (1.05, 1.45) 0.009 1.10 (1.00, 1.22) 0.056
Cases = 771 Model 2 1.21 (0.99, 1.49) 0.058 1.10 (0.97, 1.24) 0.154
Model 3 1.31 (1.11, 1.54) 0.002 1.17 (1.04, 1.31) 0.011
MetS-A
b
Model 1 1.12 (1.00, 1.24) 0.047
c
1.03 (0.95, 1.10) 0.505
Cases = 663 Model 2 1.10 (0.98, 1.24) 0.117 1.01 (0.93, 1.09) 0.899
Model 3 1.18 (1.04, 1.34) 0.011 1.05 (0.95, 1.17) 0.339
MetS-W: World Health Organization-defined metabolic syndrome. MetS-I: International Diabetes Federation-defined metabolic syndrome. Model 1: Crude;
Model 2: Model 1+ age, sex, educational attainment, neighborhood socio-economic index, occupational exposure to vapors, gases, dusts or fumes,
smoking status, smoked pack-years, exposure to passive smoke, consumption of fruits and raw vegetables, and physical activity; Model 3: Model 2+ body
mass index. PM
10
: particulate matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide. OR: odds ratio. CI: confidence interval. OR values refer
to increments of 10μg/m
3
in PM
10
and NO
2
exposure respectively. Participants’ study area was treated as a random effect in all models.
a
MetS-I defined using predicted waist circumference and European cut-off for central obesity (94cm for men and 80cm for women).
b
MetS-A defined using predicted waist circumference and North-American cut-off for central obesity (102cm for men and 88cm for women).
C
Lost statistical significance following Bonferroni correction at P<0.016 (0.05/3). PM
10
and NO
2
are not testing independent hypothesis.
doi:10.1371/journal.pone.0130337.t002
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 6/19
Associations between AP and MetS
The odds of MetS-W, MetS-I and MetS-A increased by 72% (46–102%), 31% (11–54%) and
18% (4–34%) per 10μg/m
3
increase in 10-year mean home outdoor PM
10
(Table 2). We also
observed positive but less strong associations per 10μg/m
3
increase in 10-year mean home out-
door NO
2
(Table 2).
Translated into incidence rate ratios, the risk of MetS-W, MetS-I and MetS-A increased by
52% (35–70%), 12% (4–19%), and 9% (0–19%) per 10μg/m
3
increase in 10-year mean PM
10
,
and weaker associations were also observed with NO
2
(S2 Table). Among the outcomes, we ob-
served strongest associations with MetS-W, and associations were stronger with PM
10
than
NO
2
(Table 2). Restriction of analyses to subjects reporting eight-hour fasting time provided
similar results albeit with limited statistical power. While odds ratios for MetS-W slightly de-
creased, those for MetS-I and MetS-A increased, and no asso ciation was observed between
NO
2
and MetS-A (S3 Table). In multi-pollutant MetS models, associations with PM
10
persisted
across outcomes, while those with NO
2
were strongly decreased or lost (S4 Table).
Fig 1. Effect modification by vigorous physical activity. MetS-W: Metabolic syndrome according to World
Health Organization. MetS-I: Metabolic syndrome according to International Diabetes Federation. MetS-A:
Metabolic syndrome according to Adult Treatment Panel-III criteria. Active defined as vigorous physical
activity 30 minutes per week. Inactive defined as vigorous physical activity <30minutes per week. Fully
adjusted models include age, sex, educational attainment, neighbourhood socio-economic index,
occupational exposure to vapours, gases, dusts and fumes, smoking status, smoked pack-years, exposure
to passive smoke, consumption of fruits and raw vegetables, and body mass index. PM
10
: particulate matter
<10μm in diameter from all sources. All analyses were done with four-hour fasting participants. Participants’
study area was treated as a random effect in all models. Odds ratio values refer to increments of 10μg/m
3
in
PM
10
exposure. Total N = 3684; N(physically-active) = 2115.
doi:10.1371/journal.pone.0130337.g001
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 7/19
Modification of AP and MetS association
Associations were enhanced by being physically-active (Fig 1), an ever-smoker (Fig 2) and
non-diabetic (Fig 3). We observed significant interaction between these variables and PM
10
in
association with MetS-W (P
interaction
= 0.025, 0.024 and 0.020 respectively). Similar trends were
observed with MetS-I, and associations with NO
2
even though intera ction terms were non-sig-
nificant (S5 Table). We observed no significant gender (Fig 4 and S5 Table) and age-group (Fig
5 and S5 Table) differences in the AP-MetS association, even though there was indication for a
stronger association among males and participants >50 years (Figs 4 and 5, S5 Table). With
MetS-A, there was a significant modification of NO
2
effect by age (P
interaction
= 0.021; S5
Table). Other interactions were largely non-significant (S5 Table).
Sensitivity Analyses
Estimates of associat ions were remarkably robust across sensitivity analyses. Multiple imputa-
tions of 75 observations marginally improved effect estimates. IPW adjustment for participa-
tion bias and exclusion of diabetes cases did not appreciably change these estimates (Table 3).
Ignoring study area gave very similar results as the fully-adjusted random-effects model where-
as area-specific slopes reduced the effect estimates especially for PM
10
(Table 3).
Fig 2. Effect modification by smoking status. MetS-W: Metabolic syndrome according to World Health
Organization. MetS-I: Metabolic syndrome according to International Diabetes Federation. MetS-A:
Metabolic syndrome according to Adult Treatment Panel-III criteria. Fully adjusted models include age, sex,
educational attainment, neighbourhood socio-economic index, occupational exposure to vapours, gases,
dusts and fumes, exposure to passive smoke, consumption of fruits and raw vegetables, physical activity and
body mass index. PM
10
: particulate matter <10μm in diameter from all sources. All analyses were done with
four-hour fasting participants. Participants’ study area was treated as a random effect in all models. Odds
ratio values refer to increments of 10μg/m
3
in PM
10
exposure. Total N = 3684; N(never-smoker) = 1623.
doi:10.1371/journal.pone.0130337.g002
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 8/19
We observed weaker associations with MetS-I based on BMI-defined central obesity, and
MetS-I based on North-American cut-offs for central obesity in a European population (S6
Table).
Association between AP and MetS components
There were positive associations between AP and IFG (Table 4). Associations were consistent
across exposure metrics. We also observed positive associations with hypertension, which were
strongest with NO
2
. We also found stronger associations with central obesity defined by waist
circumference compared to central obesity defined by BMI. We found no appreciable associa-
tions with other components, although eight-hour MetS estimates appeared to be stronger
than four-hour MetS estimates (Table 4).
Discussion
We found positive associations between markers of long-term AP exposure and MetS, which
were sensitive to definition in this sample of Swiss adults. Associations were most pronounced
with MetS-W, which reflects a glucose metabolism-dependent pathway, and weaker with
Fig 3. Effect modification by diabetes status. MetS-W: Metabolic syndrome according to World Health
Organization. MetS-I: Metabolic syndrome according to International Diabetes Federation. MetS-A:
Metabolic syndrome according to Adult Treatment Panel-III criteria. Fully adjusted models include age, sex,
educational attainment, neighbourhood socio-economic index, occupational exposure to vapours, gases,
dusts and fumes, smoking status, smoked pack-years, exposure to passive smoke, consumption of fruits and
raw vegetables, physical activity and body mass index. PM
10
: particulate matter <10μm in diameter from all
sources. All analyses were done with four-hour fasting participants. Participants’ study area was treated as a
random effect in all models. Odds ratio values refer to increments of 10μg/m
3
in PM
10
exposure. Total
N = 3684; N(diabetes) = 144.
doi:10.1371/journal.pone.0130337.g003
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 9/19
MetS-I which is based on visceral adiposity, and MetS-A which does not depend on a particular
pathway. Our results therefore suggest that AP seems to impact particularly on insulin resis-
tance part of MetS—aligned with impact on adipose tissue inflammation observed in animal
models [22–24] and homeostatic model of insulin resistance observed in humans [25, 26].
Given the cross-sectional nature of the analysis and the sub-group findings, one cannot derive
etiologic conclusions. But the plausibility of underlyi ng mechanisms warrants further longitu-
dinal investigations of these highly relevant results.
Potential mechanisms of action
MetS reflects a status of low grade systemic inflammation, and exposure to PM has been associ-
ated with blood markers of inflammation [27]. Exposure to PM
10
increased the expression of
inflammatory and MetS genes in mice [28]. MetS may predispose to the expression of inflam-
matory markers [14] and autonomic dysfunction [22, 23, 29] associated with chronic AP expo-
sure. The components of MetS have also been positively linked to AP. Exposure to AP has been
linked to hypertension [30, 31], alterations in blood lipids [32, 33], insulin resistance [22, 23]
and obesity [34, 35]. Exposure to passive smoke, a contributor to PM also induces inflammato-
ry responses and lipid changes, and has been positively associated with MetS-I [36]. In
Fig 4. Effect modification by sex. MetS-W: Metabolic syndrome according to World Health Organization.
MetS-I: Metabolic syndrome according to International Diabetes Federation. MetS-A: Metabolic syndrome
according to Adult Treatment Panel-III criteria. Fully adjusted models include age, educational attainment,
neighbourhood socio-economic index, occupational exposure to vapours, gases, dusts and fumes, smoking
status, smoked pack-years, exposure to passive smoke, consumption of fruits and raw vegetables, physical
activity and body mass index. PM
10
: particulate matter <10μm in diameter from all sources. NO
2
: nitrogen
dioxide. All analyses were done with four-hour fasting participants. Participants’ study area was treated as a
random effect in all models. Odds ratio values refer to increments of 10μg/m
3
in PM
10
exposure. Total
N = 3684; N(males) = 1746.
doi:10.1371/journal.pone.0130337.g004
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 10 / 19
addition, sub-acute exposures to low levels of PM
2.5
induced insulin resistance in healthy
young adults [26], whereas exposure to ambient levels of PM
10
and NO
2
induced insulin resis-
tance in children [25]. Based on the evidence from human insulin resistance studies, our find-
ing of strongest association with MetS-W and the results of the individual MetS components,
the insulin resistance pathway may be the strongest pathway through which AP exert their car-
dio-metabolic effects. This is also supported by the finding of slightly stronger association with
waist circumference-based central obesity as opposed to BMI-based central obesity, with the
former being a better indicator for insulin resistance.
Changes in inflammatory markers and blood lipids were non-significant in young adults
when exposed to AP [37]. Conve rsely, significant changes were observed in middle-aged/older
subjects, reversible with omega-3-fatty acid [38]. This supports our finding of stronger associa-
tions among older people. Smoking is a known risk factor for cardio-metabolic diseases [39],
hence our finding of stronger effect among ever-smokers may be additive effect on the already
existing effect of smoking exposure. Stronger effects among ever-smokers was observed for
MetS-W and MetS-I, but not for MetS-A. This may be explained by the facts that the never-
smokers, in our study population, were less physically-active (S7 Table) and had higher pre-
dicted waist circumference (90 vs. 88cm) compared to ever-smokers. Whereas the findings for
Fig 5. Effect modification by age group. MetS-W: Metabolic syndrome according to World Health
Organization. MetS-I: Metabolic syndrome according to International Diabetes Federation. MetS-A:
Metabolic syndrome according to Adult Treatment Panel-III criteria. Fully adjusted models include sex,
educational attainment, neighbourhood socio-economic index, occupational exposure to vapours, gases,
dusts and fumes, smoking status, smoked pack-years, exposure to passive smoke, consumption of fruits and
raw vegetables, physical activity and body mass index. PM
10
: particulate matter <10μm in diameter from all
sources. All analyses were done with four-hour fasting participants. Participants’ study area was treated as a
random effect in all models. Odds ratio values refer to increments of 10μg/m
3
in PM
10
exposure. Total
N = 3684; N(age50) = 1393.
doi:10.1371/journal.pone.0130337.g005
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MetS-W and MetS-I appear to contradict a previous finding of stronger AP effects (on diabe-
tes) among never-smokers [ 21 ], our finding with MetS-A supports it. We did not observe any
associations among the diabetes cases. This may be because of their use of medication for
blood glucose control. It may also be due their very small number which limits the statistical
power to see any associations.
We observed stronger associations among the physically active. This observation was inde-
pendent of MetS definition and persisted in the sub-sample with eight-hour fasting time.
Stronger AP associations among the physically active (with diabetes) were shown elsewhere
[10, 21]. This may be expected if the physically-active spend more time outdoors, thus, their
outdoor concentrations may better capture their actual exposure. Also, due to their deeper in-
halation while active, the physically-active have higher exposure of their lung tissues to AP for
the same ambient concentration. Physical activity improves lung function [40] and has been
shown to enhance response to volatile organic compounds [41].
As shown (S7 Table), the physically-active lived in less polluted areas. Being physically inac-
tive was also associated with areas of high outdoor PM
2.5
concentrations in normal-weight peo-
ple in previous studies [42]. One may conjecture that the observed interaction with physical
activity may be partly due to some other non-considered covariates. The inactive subjects were
exposed to other risk factors for MetS at a higher level than the active subjects (S7 Table), thus,
Table 3. Sensitivity Analyses.
10-year mean PM
10
10-year mean NO
2
MetS-W MetS-I MetS-A MetS-W MetS-I MetS-A
OR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI)
Fully-adjusted, random-effect model 1.72 (1.46,
2.02)
1.31 (1.11,
1.54)
1.18 (1.04,
1.34)
1.22 (1.02,
1.46)
1.17 (1.04,
1.31)
1.05 (0.95,
1.17)
P-value <0.001 0.002 0.011 0.033 0.011 0.339
Fully-adjusted random-effect model with multiple
imputations
1.81 (1.52,
2.15)
1.39 (1.20,
1.62)
1.17
(1.02,1.35)
1.28 (1.15,
1.43)
1.23 (1.11,
1.12)
1.07 (0.98,
1.17)
P-value <0.001 <0.001 0.021 <0.001 <0.001 0.156
IPW analysis for participation bias. 1.74 (1.49,
2.03)
1.29 (1.12,
1.49)
1.17 (1.02,
1.33)
1.31 (1.19,
1.46)
1.15 (1.04,
1.27)
1.05 (0.96,
1.15)
P-value <0.001 0.001 0.023 <0.001 0.005 0.292
Model excluding diabetes cases 1.77 (1.48,
2.12)
1.31 (1.11,
1.54)
1.16 (1.02,
1.32)
1.22 (1.00,
1.50)
1.17 (1.05,
1.32)
1.04 (0.94,
1.16)
P-value 0.020 0.356 0.994 0.110 0.091 0.597
Model excluding diabetes cases reporting
medication
1.80 (1.51,
2.14)
1.30 (1.10,
1.53)
1.17 (1.03,
1.34)
1.15 (0.92,
1.43)
1.17 (1.04,
1.32)
1.05 (0.94,
1.16)
P-value <0.001 0.002 0.015 0.226 0.009 0.421
Model, ignoring study area 1.72 (1.46,
2.02)
1.30 (1.13,
1.50)
1.18 (1.04,
1.34)
1.31 (1.18,
1.46)
1.16 (1.06,
1.28)
1.06 (0.98,
1.16)
P-value <
0.001 <0.001 0.011 <0.001 0.002 0.159
Model, including study area as fixed effect 1.10 (0.63,
2.09)
1.35 (0.86,
2.11)
1.19 (0.74,
1.91)
1.09 (0.88,
1.36)
1.21 (0.99,
1.48)
0.96 (0.79,
1.14)
P-value 0.733 0.194 0.474 0.419 0.058 0.576
Fully adjusted models include age, sex, educational attainment, neighbourhood socio-economic index, occupational exposure to vapours, gases, dusts
and fumes, smoking status, smoked pack-years, exposure to passive smoke, consumption of fruits and raw vegetables, physical activity and body mass
index. MI: multiple imputations. IPW: inverse probability weighting. PM
10
: particulate matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide.
OR: odds ratio. CI: confidence interval. OR refer to increments of 10μg/m
3
in PM
10
, and NO
2
exposure respectively. All analyses were done with four-hour
fasting participants.
doi:10.1371/journal.pone.0130337.t003
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PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 12 / 19
the relative role of AP in MetS development may be less crucial in them. Use of more objective
measures of visceral adiposity should improve the definition of MetS.
Strengths and Limitations
This study derives from the large SAPALDIA database, with detailed information on health,
socio-demographic and lifestyle characteristics. This allowed us to have a clean case definition
and detailed confounder adjustment. We had validated annual estimates of residential AP ex-
posures from which long-term exposure estimates were derived. To the best of our knowledge,
this is the first study to assess direct associations between AP and MetS. Its results may help in
understanding the pathways involved in the effects of AP on cardiovascular disease
and diabetes.
A major limitation is the cross-sectional design which precludes etiologic inferences. We
did not measure waist circumference at this visit but had a validated prediction model based on
trends at the next follow-up visit. As we do not have urinary albumin excretion ratio for our
participants, we may have misclassified some MetS-W cases. We used four-hour fasting time
to define MetS, instead of conventional eight hours in our main analysis. This was due to the
small sample of participants who reported a fasting time of at least eight hours, limiting our sta-
tistical power. However, associations were also positive in the subjects who fasted for eight
hours. Four-hour fasting blood samples can be used for patient diagnosis in ambulatory set-
tings [43]. Also, non-fasting triglycerides were shown to be a predictor of cardiac events in
Table 4. Association between air pollutants and components of metabolic syndrome.
Fasting time
(hours)
10-year mean PM
10
OR (95%CI)
P-value 10-year mean NO
2
OR (95%CI)
P-value
Impaired fasting Glycaemia (IFG;WHO) 4 1.82 (1.60, 2.08) <0.001 1.15 (0.98, 1.34) 0.080
8 2.27 (1.43, 3.62) 0.001 1.33 (0.98, 1.79) 0.063
Impaired fasting Glycaemia (IFG; IDF/ATP-III) 4 1.45 (1.19, 1.78) <0.001 1.06 (0.93, 1.21) 0.388
8 1.84 (1.30, 2.60) 0.001 1.36 (1.08, 1.72) 0.008
Low high-density lipoproteins (WHO) 4 0.95 (0.76, 1.19) 0.657 0.88 (0.76, 1.01) 0.071
8 0.89 (0.47, 1.70) 0.735 0.76 (0.49, 1.19) 0.229
Low high-density lipoproteins (IDF/ATP-III) 4 0.99 (0.87, 1.12) 0.847 0.95 (0.87, 1.05) 0.303
8 0.99 (0.63, 1.56) 0.982 0.86 (0.66, 1.13) 0.287
High triglycerides 4 0.90 (0.77, 1.05) 0.169 0.94 (0.85, 1.03) 0.194
8 1.14 (0.78, 1.67) 0.494 0.94 (0.73, 1.21) 0.630
Hypertension (WHO) 4 1.12 (0.97, 1.29) 0.130 1.11 (1.01, 1.20) 0.022
Hypertension (IDF/ATP-III) 4 1.11 (0.95, 1.30) 0.172 1.12 (1.03, 1.23) 0.011
Central obesity (BMI>30kg/m2) 4 1.00 (0.83, 1.21) 0.971 1.01 (0.89, 1.14) 0.898
Central obesity
a
4 1.19 (0.90, 1.58) 0.218 1.06 (0.90, 1.26) 0.465
Fully adjusted models include age, sex, educational attainment, neighbourhood socio-economic index, occupational exposure to vapours, gases, dusts
and fumes, smoking status, smoked pack-years, exposure to passive smoke, consumption of fruits and raw vegetables, physical activity and body mass
index (BMI). Model for obesity excludes BMI. PM
10
: particulate matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide. TrafficPM
10
refers to
dispersion models including only traffic-related emissions. OR: odds ratio. CI: confidence interval. OR values represent fold increase in odds of outcomes
per 10 μg/m
3
of PM
10
,NO
2
, and 1μg/m
3
of trafficPM
10
exposure. IFG defined as fasting blood glucose6.1mmol/L and/or diagnosis of type2diabetes.
High triglycerides defined as fasting triglycerides1.7mmol/L or treatment for this condition. Low HDL defined by IDF and ATP-III as < 1.03 mmol/L
(males), < 1.29 mmol/L (females), or treatment for this condition, and by WHO as 0.9 mmol/L (males), 1.0 mmol/L (females). Hypertension defined by
IDF and ATP-III as blood pressure >130/85 mm Hg and by WHO as 140/90, or treatment of previously diagnosed hypertension. Participants’ study area
was treated as a random effect in all models. N (4 hours fasting time) = 3684. N (8 hours fasting time) = 367.
a
Central obesity defined using the predicted waist circumference and European cut-offs (94cm for males and 80cm for females)
doi:10.1371/journal.pone.0130337.t004
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 13 / 19
women [44]. PM
2.5
was not modelled in this study, thus we relied on PM
10
. While one may
argue PM
2.5
to be more relevant for systemic effects, the lack thereof, is unlikely to bias this
analysis. In Switzerland, PM
2.5
contributes 70–80% to the PM
10
fraction and both are highly
correlated within and across SAPALDIA areas (R~0.8).
We used two markers of ambient pollution with partly different characteristics. Our results
indicate possible larger effects of PM
10
compared to NO
2
. This may largely be because PM
10
represents a mixture of different particles, unlike NO
2
which measures a specific gas. Particu-
late matter has been shown to be stronger activators of innate immunity in comparison with
gaseous pollutants [22, 23].
We did not have estimates of indoor or occupational AP for our participants, but any mis-
classification that could be caused by this is expected to be non-systematic, leading to a null
bias. We considered occupational exposure to VGDF, which partly adjusts for indoor occupa-
tional exposure. Only 46% of follow-up and 38% of baseline participants was studied. A sub-
stantial percentage of non-inclusion was due to subjects who had venepuncture in less than
four-hour fasting time. Despite this low participation, all study areas and other characteristics
were well represented in this study sample. Sensitivity analyses using IPW suggested that par-
ticipation bias was non-substantial. Despite this finding, some bias may still persist. The weaker
precision from the fixed effect model, especially for PM
10
, could be due to poor within-area
spatial contrasts exhibited by PM
10
compared to the traffic-related exposures [10, 45].
It is unclear if the associations with PM
10
are due to the inflammation elicited by physical ef-
fects of particles and/or the innate immunity response elicited by its biological components.
These and other questions deserve further investigation by future well-designed longitudinal
studies. The studies should consider measured waist circumference as a component of MetS,
and explore associations with PM comp onents. Also, physical activity must be more objectively
measured. Our findings, if confirmed, are of great public health relevance, as they may call for
physical activity promotion to be adapted to various environmental contrasts.
Supporting Information
S1 Table. Characteristics of participants included and excluded in the study. ETS: environ-
mental tobacco smoke. VGDF: vapours, gases, dusts and fumes. MVPA: moderate to vigorous
physical activity. Hypertension defined as blood pressure >130/85 mm Hg or treatment of pre-
viously diagnosed hypertension. SEI: socio-economic index expressed as a percentage. PM
10
:
particulate matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide.
(DOCX)
S2 Table. Incidence rate ratios of metabolic syndrome in associ ation with air pollutants.
MetS-W: World Heal th Organization-defined metabolic syndrome. MetS-I: International Dia-
betes Federation-defined metabolic syndrome. Model 1: Crude; Model 2: Model 1+ age, sex,
educational attainment, neighbourhood socio-economic index, occupational exposure to va-
pours, gases, dusts or fumes, smoking status, smoked pack-years, exposure to passive smoke,
consumption of fruits and raw vegetables, and physical activity; Model 3: Model 2+ body mass
index. PM
10
: particulate matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide.
OR: odds ratio. CI: confidence interval. OR values refer to increments of 10μg/m
3
in PM
10
and
NO
2
exposure respectively. Participants’ study area was treated as a random effect in all mod-
els. N = 3684
(DOCX)
S3 Table. Association between air pollutants and metabolic syndrome (8 hours fasting
time). MetS-W: World Health Organization-defined metabolic syndrome. MetS-I:
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 14 / 19
International Diabetes Federation-defined metabolic syndrome. Model 1: Crude; Model 2:
Model 1+ age, sex, educational attainment, neighbourhood socio-economic index, occupation-
al exposure to vapours, gases, dusts or fumes, smoking status, smoked pack-years, exposure to
passive smoke, consumption of fruits and raw vegetables, and physical activity; Model 3:
Model 2+ body mass index. PM
10
: particulate matter <10μm in diameter from all sources.
NO
2
: nitrogen dioxide. OR: odds ratio. CI: confidence interval. OR values refer to increments
of 10μg/m
3
in PM
10
and NO
2
exposure respectively. Participants’ study area was treated as a
random effect in all models. N = 367
(DOCX)
S4 Table. Association between air pollutants and metabolic syndrome (two-pollutant mod-
els). MetS-W: World Health Organization-defined metabolic syndrome. MetS-I: International
Diabetes Federation-defined metabolic syndrome. Model 1: Crude; Model 2: Model 1+ age,
sex, educational attainment, neighbourho od socio-economic index, occupational exposure to
vapours, gases, dusts or fumes, smoking status, smoked pack-years, exposure to passive smoke,
consumption of fruits and raw vegetables, and physical activity; Model 3: Model 2+ body mass
index. PM
10
: particulate matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide.
OR: odds ratio. CI: confidence interval. OR values refer to increments of 10μg/m
3
in PM
10
and
NO
2
exposure respectively. Participants’ study area was treated as a random effect in all mod-
els. N = 3684
(DOCX)
S5 Table. Effect modification of NO
2
and metabolic syndrome association. Fully adjusted
models include age, sex, educational attainment, neighbourhood socio-economic index, occu-
pational exposure to vapours, gases, dusts and fumes, smoking status, smoked pack-years, ex-
posure to passive smoke, consumption of fruits and raw vegetables, physical activity and body
mass index. NO
2
: nitrogen dioxide. All analyses were done with four-hour fasting participants.
Participants’ study area was treated as a random effect in all models. OR: Odds ratios OR values
refer to increments of 10μg/m
3
in NO
2
exposure. Total N = 3684; N(age50) = ; N(males) =
1746; N(physically-active) = 2115; N(never-smoker) = 1623; N(diabetes) = 144.
(DOCX)
S6 Table. Association between air pollutants and alternative definitions of metabolic syn-
drome. MetS-W: World Health Organization-defined metabolic syndrome. MetS-I: Interna-
tional Diabetes Federation-defined metabolic syndrome. MetS-ATP-III: Adult treatment panel
III criteria- defined metabolic syndrome. Model 1: Crude; Model 2: Model 1+ age, sex, educa-
tional attainment, neighbourhood socio-economic index, occupational exposure to vapours,
gases, dusts or fumes, smoking status, smoked pack-years, exposure to passive smoke, con-
sumption of fruits and raw vegetables, and physical activity; Model 3: Model 2+ body mass
index. PM
10
: particulate matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide.
OR: odds ratio. CI: confidence interval. OR values refer to increments of 10μg/m
3
in PM
10
and
NO
2
exposure respectively. Participants’ study area was treated as a random effect in all mod-
els. N (Four-hour fasting time) = 3684; N (Eight-hour fasting time) = 367.
(DOCX)
S7 Table. Participants’ characteristics by self-reported physical activity. MetS-W: Metabolic
syndrome according to World Health Organization. MetS-I: Metabolic syndrome according to
International Diabetes Federation. MetS-A: Metabolic syndrome according to Adult Treatment
Panel III criteria. ETS: environmental tobacco smoke. VGDF: vapours, gases, dusts and fumes.
IFG defined as fasting blood glucose6.1mmol/L and/or diagnosis of type2diabetes. High tri-
glycerides defined as fasting triglycerides1.7mmol/L or treatment for this condition. Low
Ambient Air Pollution and Metabolic Syndrome
PLOS ONE | DOI:10.1371/journal.pone.0130337 June 23, 2015 15 / 19
HDL defined by IDF as < 1.03 mmol/L (males), < 1.29 mmol/L (females), or treatment for
this condition, and by WHO as 0.9 mmol/L (males), 1.0 mmol/L (females). Hypertension
defined by IDF and ATP-III as blood pressure >130/85 mm Hg and by WHO as 140/90, or
treatment of previously diagnosed hypertension. SEI: socio-economic index. PM
10
: particulate
matter <10μm in diameter from all sources. NO
2
: nitrogen dioxide.
(DOCX)
Acknowledgments
We thank all participants and field workers in the Swiss Cohort Study on Air pollution and
Lung and Heart Diseases in Adults [SAPALDIA] team for their time, commitment and work.
SAPALDIA Team: Study directorate: NM Probst-Hensch (PI; e/g); T Rochat (p), N Künzli
(e/exp), C Schindler (s), JM Gaspoz (c). Scientific team: JC Barthélémy (c), W Berger (g), R
Bettschart (p), A Bircher (a), O Brändli (p), C Brombach (n), L Burdet (p), M Frey (p), U Frey
(pd), MW Gerbase (p), D Gold (e), E de Groot (c), W Karrer (p), M Kohler (p), B Martin (pa),
D Miedinger (o), L Nicod (p), M Pons (p), F Roche (c), T Rothe (p), P Schmid-Grendelmeyer
(a), A Schmidt-Truc ksäss (pa), A Turk (p), J Schwartz (e), D. Stolz (p), P Straehl (exp), JM
Tschopp (p), A von Eckardstein (cc), E Zemp Stutz (e). Scientific team at coordinating centers:
M Adam (e/g), I Aguilera, C Autenrieth (pa), PO Bridevaux (p), D Carballo (c), I Curjuric (e), J
Dratva (e), R Ducret (s), E Dupuis Lozeron (s), M Eeftens (exp), I Eze (e), E Fischer (g), M Ger-
mond (s), L Grize (s), S Hansen (e), A Hensel (s), M Imboden (g), A Ineichen (exp), D Keidel
(s), A Kumar (g), N Maire (s), A Mehta (e), R Meier (exp), E Schaffner (s), T Schikowski (e),
GA Thun (g), M Tarantino (s), M Tsai (e)
(a) allergology, (c) cardiology, (cc) clinical chemistry, (e) epidemiology, (exp) exposure, (g)
genetic and molecular biology, (m) meteorology, (n) nutrition, (o) occupational health, (p)
pneumology, (pa) physical activity, (pd) pediatrics, (s) statistics. Local fieldworkers: Aarau: S
Brun, G Giger, M Sperisen, M Stahel, Basel: C Bürli, C Dahler, N Oertli, I Harreh, F Karrer, G
Novicic, N Wyttenbacher, Davos: A Saner, P Senn, R Winzeler, Geneva: F Bonfils, B Blicharz,
C Landolt, J Rochat, Lugano: S Boccia, E Gehrig, MT Mandia, G Solari, B Viscardi, Montana:
AP Bieri, C Darioly, M Maire, Payerne: F Ding, P Danieli A Vonnez, Wald: D Bodmer, E Hoch-
strasser, R Kunz, C Meier, J Rakic, U Schafroth, A Walder. Administrative staff: C Gabriel, R
Gutknecht.
Author Contributions
Conceived and designed the experiments: NPH. Performed the experiments: NPH ICE ES MF
MI AVE T. Rochat T. Rothe MWG NK CS. Analyzed the data: NPH ICE ES CS. Contributed
reagents/materials/analysis tools: NPH. Wrote the paper: NPH ICE ES MF MI AVE T. Rochat
T. Rothe MWG NK CS.
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