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Citation: Kuo, C.-L.; Liu, R.; Godoy,
L.d.C.; Pilling, L.C.; Fortinsky, R.H.;
Brugge, D. Association between
Residential Exposure to Air Pollution
and Incident Coronary Heart Disease
Is Not Mediated by Leukocyte
Telomere Length: A UK Biobank
Study. Toxics 2023,11, 489.
https://doi.org/10.3390/
toxics11060489
Academic Editor: Nan Sang
Received: 6 April 2023
Revised: 16 May 2023
Accepted: 23 May 2023
Published: 28 May 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
toxics
Article
Association between Residential Exposure to Air Pollution and
Incident Coronary Heart Disease Is Not Mediated by Leukocyte
Telomere Length: A UK Biobank Study
Chia-Ling Kuo 1,2,3,*, Rui Liu 4, Lucas da Cunha Godoy 1, Luke C. Pilling 5, Richard H. Fortinsky 3
and Doug Brugge 2
1The Cato T. Laurencin Institute for Regenerative Engineering, University of Connecticut Health,
Farmington, CT 06030, USA
2Department of Public Health Sciences, University of Connecticut Health, Farmington, CT 06032, USA
3UConn Center on Aging, University of Connecticut Health, Farmington, CT 06030, USA
4Department of Health Sciences, Sacred Heart University, Fairfield, CT 06825, USA
5Epidemiology and Public Health Group, Faculty of Health and Life Sciences, University of Exeter,
Exeter EX1 2LU, UK
*Correspondence: kuo@uchc.edu
Abstract:
Higher air pollution exposure and shorter leukocyte telomere length (LTL) are both associ-
ated with increased risk of coronary heart disease (CHD), and share plausible mechanisms, including
inflammation. LTL may serve as a biomarker of air pollution exposure and may be intervened with
to reduce the risk of CHD. To the best of our knowledge, we are the first to test the mediation effect
of LTL in the relationship between air pollution exposure and incident CHD. Using the UK Biobank
(UKB) data (n= 317,601), we conducted a prospective study linking residential air pollution exposure
(PM
2.5
, PM
10
, NO
2
, NO
x
) and LTL to incident CHD during a mean follow-up of 12.6 years. Cox
proportional hazards models and generalized additive models with penalized spline functions were
used to model the associations of pollutant concentrations and LTL with incident CHD. We found
non-linear associations of air pollution exposure with LTL and CHD. Pollutant concentrations in the
lower range were decreasingly associated with longer LTL and reduced risk of CHD. The associa-
tions between lower pollutant concentrations and reduced risk of CHD, however, were minimally
mediated by LTL (<3%). Our findings suggest that air pollution influences CHD through pathways
that do not involve LTL. Replication is needed with improved measurements of air pollution that
more accurately assesses personal exposure.
Keywords: PM2.5; PM2.5 absorbance; PM10 ; PM2.5–10; NO2; NOx
1. Introduction
Ambient air pollution, including particulate matter (PM) and oxides of nitrogen
(NO
2
and NO
x
) have been consistently and, in the case of PM
2.5
, causally associated
with cardiovascular disease, including coronary heart disease (CHD) [
1
–
3
]. Due largely
to cardiovascular health outcomes, PM
2.5
is one of the leading causes of morbidity and
mortality globally [
4
,
5
]. However, the biological pathways and mechanisms by which
PM drives health outcomes remain under investigation, with many pathways, including
inflammation [6–8].
Telomeres are repetitive base pair sequences at the end of chromosomes [
9
] that
shorten with age and lead to cell cycle arrest and apoptosis when reaching a critical
point [
10
]. Senescent cells secrete high levels of inflammatory cytokines, cell cycle regulators,
growth factors, and tissue remodeling factors [
11
], which can contribute to cardiovascular
disease [
12
]. The association of shorter leukocyte telomere length (LTL) with CHD, likely
causal, is consistently replicated by observational studies and confirmed by Mendelian
randomization studies that are robust to reverse causation and confounding [13–15].
Toxics 2023,11, 489. https://doi.org/10.3390/toxics11060489 https://www.mdpi.com/journal/toxics
Toxics 2023,11, 489 2 of 14
Air pollution shares plausible mechanisms underlying the association between shorter
LTL and CHD, including oxidative stress [
16
], chronic inflammation [
17
], and endothelial
cell senescence [
18
,
19
]. It seems plausible that exposure to air pollution accelerates telomere
shortening, which would, in turn, increase the risk of CHD. LTL, therefore, may serve
as a biomarker of air pollution exposure and a prognostic factor for CHD. However, the
association of exposure to air pollution with telomere length remains inconclusive [
20
–
22
].
We hypothesized that increased air pollution exposure is associated with CHD, and shorter
LTL partially mediates the association.
To test our hypothesis, we conducted a prospective study linking residential air
pollution exposure to LTL and incident CHD. We tested the mediation effect of LTL in the
association between air pollution exposure and incident CHD during a mean follow-up of
12.6 years in the UK Biobank (UKB) cohort [
23
,
24
]. To the best of our knowledge, this is
the first large-scale population study to explore the possible role of telomere biology in the
association between air pollution exposure and the risk of CHD. Evidence of a mediating
role of telomere length would support monitoring LTL for the risk of CHD due to exposure
to air pollution.
2. Materials and Methods
2.1. UK Biobank
Data were obtained from the UKB, a large volunteer cohort in the United Kingdom.
Over 500,000 participants were recruited from 2006 to 2010 with ages between 40 and
70 [
23
,
24
]. About 95% of the cohort are of European descent. At recruitment (baseline),
participants completed online questionnaires, tests, and verbal interviews. They also per-
formed physical assessments and provided biological samples for future assays. Through
linkages to external datasets and electronic health records, additional data are available,
including residential air pollution estimates and longitudinal follow-ups of disease diag-
noses and death. Additional details about the cohort are described elsewhere, e.g., [
23
,
24
]
and the UKB website [25].
2.2. Inclusion and Exclusion Criteria
We considered all the active UKB participants (n= 503,398) and excluded participants
who had a diagnosis of CHD or cancer (excluding non-melanoma skin cancer) at or prior
to baseline. Cancer is a potential confounder for the reported associations with longer
LTL [
14
] and incident CHD [
26
]. We also excluded those with any missing values for
air pollution, LTL, or covariates (n= 184,797) (Figure 1). Participants who did not have
any CHD diagnosis in the records were assumed to have not developed CHD. A total of
317,601 study participants were included in our analysis.
2.3. Data
Air pollution, LTL, and covariates selected as potential confounders for associations
with CHD were measured at baseline. Incident CHD during follow-up was the outcome
of interest for associations with air pollution exposure and LTL to conduct a mediation
analysis.
The timeline in Figure 2illustrates the data collection. Data were extracted using the
field IDs in Table S1. As shown in Figure 2, the censoring date for incident coronary heart
disease varied with data providers. It was set to 30 September 2021 in line with that of the
Hospital Episode Statistics for England (HES), which was the main source used to confirm
disease diagnoses [27]. Other data were collected at the recruitment/baseline of UKB.
Toxics 2023,11, 489 3 of 14
Toxics 2023, 11, x FOR PEER REVIEW 3 of 16
Figure 1. Sample selection flowchart.
2.3. Data
Air pollution, LTL, and covariates selected as potential confounders for associations
with CHD were measured at baseline. Incident CHD during follow-up was the outcome
of interest for associations with air pollution exposure and LTL to conduct a mediation
analysis.
The timeline in Figure 2 illustrates the data collection. Data were extracted using the
field IDs in Table S1. As shown in Figure 2, the censoring date for incident coronary heart
disease varied with data providers. It was set to 30 September 2021 in line with that of the
Hospital Episode Statistics for England (HES), which was the main source used to confirm
disease diagnoses [27]. Other data were collected at the recruitment/baseline of UKB.
Figure 2. A timeline to illustrate the data collection.
Figure 1. Sample selection flowchart.
Toxics 2023, 11, x FOR PEER REVIEW 3 of 16
Figure 1. Sample selection flowchart.
2.3. Data
Air pollution, LTL, and covariates selected as potential confounders for associations
with CHD were measured at baseline. Incident CHD during follow-up was the outcome
of interest for associations with air pollution exposure and LTL to conduct a mediation
analysis.
The timeline in Figure 2 illustrates the data collection. Data were extracted using the
field IDs in Table S1. As shown in Figure 2, the censoring date for incident coronary heart
disease varied with data providers. It was set to 30 September 2021 in line with that of the
Hospital Episode Statistics for England (HES), which was the main source used to confirm
disease diagnoses [27]. Other data were collected at the recruitment/baseline of UKB.
Figure 2. A timeline to illustrate the data collection.
Figure 2. A timeline to illustrate the data collection.
2.3.1. Residential Air Pollution
The air pollution monitoring data were collected in 2010 by the Small Area Health
Statistics Unit [
28
] as part of the BioSHaRE-EU Environmental Determinants of Health
Project [
29
]. Air pollution concentrations were modeled for nitrogen dioxide (NO
2
,
µ
g/m
3
),
nitrogen oxides (NO
x
;
µ
g/m
3
), particulate matter of less than 10 um (PM
10
;
µ
g/m
3
),
PM2.5 (µg/m3)
, PM
2.5
absorbance (per meter), and PM
2.5–10
(
µ
g/m
3
) using Land Use Re-
gression (LUR) models (resolution 100 m
×
100 m) provided by the European Study of
Cohorts for Air Pollution Effects (ESCAPE, [
30
]) [
31
,
32
]. Notably, the ESCAPE estimates
for particulates (PM
10
, PM
2.5
, PM
2.5
absorbance, and PM coarse concentrations) more than
400 km away from the monitoring area, i.e., Greater London, might be invalid and were
set to missing (n= 33,935) in the data UKB released. For sensitivity analysis, we analyzed
data of PM
10
and NO
2
from previous years (prior to 2010) that were derived from EU-wide
Toxics 2023,11, 489 4 of 14
air pollution maps based on a LUR model (resolution 100 m
×
100 m) [
33
]. The multi-year
data were averaged, excluding data from 2010 to avoid potential batch effects.
2.3.2. Leukocyte Telomere Length
Relative mean LTL (referred to simply as LTL hereafter) was measured from peripheral
blood leukocytes as a T/S ratio using a multiplex qPCR technique by comparing the amount
of the telomere amplification product (T) to that of a single-copy gene (S). LTL was used in
this project after adjusting for the influence of technical parameters, as recommended by
UKB [34].
2.3.3. Disease Diagnoses
CHD diagnoses were confirmed based on ICD-10 codes (I20–I25) using the first occur-
rence data derived by UKB that linked multi-source data, including primary care, hospital
inpatient, death register, and baseline self-reported medical condition data. First diagnosis
dates were extracted for CHD cases to compare with the baseline assessment dates to deter-
mine a prevalent case at baseline or an incident case during follow-up. Throughout the
study, CHD-free participants were censored at the last follow-up date of HES in England or
the date of death, depending on which occurred first.
Cancer diagnoses are not included in the first occurrence data. Using cancer registry,
hospital inpatient, and baseline self-reported medical condition data, participants diag-
nosed with any cancer (excluding non-melanoma skin cancer, ICD-10 C00–C97 excluding
C44) at or prior to baseline were excluded from analysis.
2.3.4. Covariates
Socio-demographic data included age, self-reported sex (male or female), ethnicity
(grouped into White, Black, South Asian, and Other) and education (from none to college or
university degree). The percentage of greenspace as a proportion of all land-use types was
estimated at 1000 m buffers from the home address [
35
]. Lifestyle factors included body
mass index (BMI), smoking status, alcohol intake frequency, and physical activity. Weight
and height used to calculate BMI were physically measured at recruitment. Smoking status
(never, former, or current) and alcohol intake frequency (never, special occasions only, one
to three times a month, one or twice a week, three or four times a week, daily or almost
daily) were assessed via online questionnaires. The physical activity group (low, moderate,
or high) was self-reported and measured following the short International Physical Activity
Questionnaire guideline [36].
2.4. Statistical Methods
Participant characteristics were descriptively summarized by the status of incident
CHD. The groups with and without incident CHD were compared for categorical variables
using chi-square tests and two-sided Wilcoxon rank-sum tests for continuous variables.
Histograms were plotted to visualize the distributions of air pollutants. Air pollutant
concentrations were correlated with each other using Spearman’s rank-based correlation.
Data of LTL and air pollutant concentrations were z-transformed according to the in-
verse normal transformation prior to the association analysis. Associations of air pollutants
with LTL were examined using generalized additive models (GAMs). Cox proportional
hazards models were used for LTL or air pollutants and incident CHD. The associations
above were adjusted for covariates (age, sex, ethnicity, education, BMI, smoking status,
alcohol intake frequency, physical activity group, and percent of greenspace percentage in
1000 m buffers), allowed to be nonlinear and modeled via penalized cubic spline functions
(number of splines in the basis 10). The models above were fitted using the R functions:
“cs” and “gam”, and “pspline” and “coxph”.
LTL and one air pollutant at a time were modeled jointly in a Cox proportional hazards
model to explore the mediation effect of LTL in the association between the pollutant
concentration and incident CHD. Additionally, we conducted a mediation analysis to test
Toxics 2023,11, 489 5 of 14
if the association between air pollution in the lower exposure range and protection of CHD
was mediated by longer LTL. Due to a lack of tools to tackle the challenge of intensive
computation, we conducted a mediation analysis using a linear regression model instead
of a generalized additive model to model the association between air pollution and LTL,
and a logistic regression model instead of a Cox regression model including a cubic spline
function to model the association of air pollution and LTL with incident CHD. Specifically,
we categorized the z-scores of each air pollutant into the ranges of (
−
Inf,
−
2], (
−
2,
−
1],
(
−
1,
−
0.5], (
−
0.5, 0.5], (0.5, 1], (1, 2], and (2, Inf]. A linear regression model was used to
model the mediation of LTL by comparing the mean LTL of a group with a lower range of
exposure to air pollution ((
−
Inf,
−
2], (
−
2,
−
1], or (
−
1,
−
0.5]) to that of the reference group
(
−
0.5, 0.5) with average exposure. A logistic regression model was used to model incident
CHD including LTL and a level of pollutant concentration below the average. Both models
were adjusted for covariates. The direct and indirect effects of a low pollutant concentration
and proportion of effect mediated by LTL were reported by the status of incident CHD and
on average. The mediation analysis was carried out using the R package “mediation” (a
quasi-Bayesian approximation method to estimate confidence intervals) [37].
3. Results
3.1. Descriptive Analysis
The CHD incidence was 7.3% during a mean follow-up of 12.6 years (SD = 0.79),
with the mean age at diagnosis 67.3 years (SD = 7.6). Older adults (median baseline age
61 years
in CHD cases versus 56 years in CHD-free controls), men (10.2% versus 4.8% in
women), and South Asian people (10.9% versus 7.3% in White people and 4.3% in Black
people) were at higher risk of incident CHD (Table 1). Higher education and healthier
lifestyles (lower BMI, no smoking, higher physical activity, and percentage of greenspace)
were associated with a lower incidence of CHD (Table 1). In contrast, moderate alcohol
consumption (
1–3 times
a month to 3–4 times a week to) was protective of incident CHD
(Table 1).
Table 1. Participant characteristics of the included samples (n= 317,601).
Characteristics Incident CHD
(n= 23,089) No Incident CHD (n= 294,512) p-Value
Baseline age, years (median (first quartile,
third quartile)) 61 (56, 65) 56 (49, 62) <0.001
Sex (%) <0.001
Male 14,868 (10.2%) 131,231 (89.8%)
Female 8221 (4.8%) 163,281 (95.2%)
Ethnicity (%) <0.001
White 21,859 (7.3%) 278,118 (92.7%)
Black 229 (4.3%) 5144 (95.7%)
South Asian 690 (10.9%) 5614 (89.1%)
Other 311 (5.2%) 5636 (94.8%)
Education (%) <0.001
None 5448 (11.7%) 41,167 (88.3%)
CSEs or equivalent 771 (6.1%) 11,930 (93.9%)
O levels/GCSEs or equivalent 2948 (7.0%) 39,262 (93.0%)
A/AS levels/NVQ/HND/HNC 4273 (7.2%) 55,293 (92.8%)
Other professional qualifications 3523 (7.4%) 44,089 (92.6%)
College or university degree 6126 (5.6%) 102,771 (94.4%)
BMI, kg/m2(mean ±SD) 27.83 (25.22, 31) 26.43 (23.91, 29.45)
Smoking status (%) <0.001
Never 10,715 (6.0%) 167,879 (94.0%)
Previous 9205 (8.6%) 98,448 (91.4%)
Current 3169 (10.1%) 28,185 (89.9%)
Toxics 2023,11, 489 6 of 14
Table 1. Cont.
Characteristics Incident CHD
(n= 23,089) No Incident CHD (n= 294,512) p-Value
Alcohol intake frequency (%) <0.001
Never 2141 (9.4%) 20,746 (90.6%)
Special occasions only 2714 (8.0%) 31,224 (92.0%)
1–3 times a month 2379 (6.8%) 32,646 (93.2%)
1–2 times a week 5665 (6.9%) 76,576 (93.1%)
3–4 times a week 5021 (6.6%) 71,434 (93.4%)
Daily or almost daily 5169 (7.7%) 61,886 (92.3%)
IPAQ activity group (%) <0.001
Low 3729 (8.1%) 42,379 (91.9%)
Moderate 10,162 (7.3%) 129,905 (92.7%)
High 9198 (7.0%) 122,228 (93.0%) <0.001
Greenspace percentage (buffer 1000 m) 42.22 (28.11, 59.68) 41.89 (27.4, 60.59) 0.161
Telomere length (T/S ratio), adjusting for
technical parameters 0.80 (0.73, 0.88) 0.83 (0.75, 0.91) <0.001
NO2in 2010 (µg/m3)26.24 (21.58, 31.22) 26.05 (21.22, 31.23) <0.001
NO2in 2005 (µg/m3)28.89 (23.55, 35.22) 28.67 (23.19, 35.37) 0.028
NO2in 2006 (µg/m3)28.28 (23.15, 33.79) 28.06 (22.81, 33.77) 0.002
NO2in 2007 (µg/m3)29.43 (24.14, 35.49) 29.29 (23.75, 35.65) 0.099
NO2avg. (2005, 2006, 2007) (µg/m3)28.86 (23.65, 34.81) 28.71 (23.28, 34.9) 0.023
NOxin 2010 (µg/m3)42.39 (34.61, 50.85) 42 (33.93, 50.64) <0.001
PM2.5 in 2010 (µg/m3)9.95 (9.32, 10.58) 9.91 (9.27, 10.55) <0.001
PM2.5 absorbance in 2010 (per meter) 1.13 (1.00, 1.3) 1.13 (0.99, 1.31) 0.386
PM2.5–10 in 2010 (µg/m3)6.11 (5.85, 6.63) 6.11 (5.84, 6.63) 0.169
PM10 in 2007 (µg/m3)21.96 (20.59, 23.7) 22.00 (20.56, 23.89) 0.002
PM10 in 2010 (µg/m3)16.03 (15.27, 16.98) 16.02 (15.23, 16.99) 0.038
Shorter LTL was observed in CHD cases (median 0.80 (T/S ratio)) than in CHD-
free controls (median 0.83 (T/S ratio)) (Table 1). PM
2.5
, NO
2
, and NO
x
in 2010 were
higher at the residence of CHD cases than those at the residence of CHD-free controls
(Table 1). The distributions of LTL and pollutant concentrations were somewhat right
skewed (Figure S1). Air pollutant concentrations were positively correlated with each other
(Figure S2). PM
2.5
, NO
2
, and NO
x
in 2010 were highly correlated (Spearman r> 0.85) as
were PM
2.5–10
and PM
10
in 2010 (Spearman r= 0.77). Interestingly, the correlation of PM
10
concentrations between 2007 and 2010 was low (Spearman r= 0.43) in contrast with NO
2
average concentrations in 2005–2007 and 2010 (Spearman r= 0.88).
3.2. Associations of Air Pollutants with Leukocyte Telomere Length
The associations between pollutant concentrations and LTL showed significant non-
linearity in unadjusted models (effective degrees of freedom (edf) >> 1 and non-linearity
p< 0.05
in Figure S3) with wide confidence intervals at high and low concentrations due to
smaller sample sizes. After adjusting for covariates, the non-linearity and the partial effect
of the air pollutant on LTL were attenuated, but most non-linear associations remained
except for PM
2.5–10
in 2010 (p> 0.05). As shown in Figure 3, an upward trend on the left
indicated that NO
2
, NO
x
, PM
2.5
, and PM
2.5
absorbance concentrations in the lower range
were associated with longer LTL. Higher concentrations of these air pollutants did not
appear to be associated with LTL based on the majority of the data (z-scores between
0 and 2
). The associations of extremely high pollutant concentrations (z-scores > 2) with
LTL were uncertain, as reflected in the wide confidence intervals and substantial deviations
from monotonic curves. Interestingly, PM
10
showed a U-shaped relationship with LTL, but
again with substantial uncertainty.
Toxics 2023,11, 489 7 of 14
Toxics 2023, 11, x FOR PEER REVIEW 8 of 16
Figure 3. Generalized additive model (GAM) plots of partial effects of pollutant concentrations on
leukocyte telomere length (LTL) adjusting for covariates (age, sex, ethnicity, education, BMI, smok-
ing status, alcohol intake frequency, physical activity, percent of greenspace in 1000 m buffers). The
tick marks on the x-axis are z-scores of the concentration of an air pollutant. The y-axis represents
the partial effect of the concentration of an air pollutant. The areas between dashed lines indicate
the 95% confidence intervals. (a) NO2 in 2010 and LTL; (b) NO2 average 2005–2007 and LTL; (c) NOx
in 2010 and LTL; (d) PM2.5 in 2010 and LTL; (e) PM2.5 absorbance in 2010 and LTL; (f) PM2.5–10 in 2010
and LTL; (g) PM10 in 2007 and LTL; (h) PM10 in 2010 and LTL.
3.3. Associations of Leukocyte Telomere Length and Air Pollutants with Incident Coronary Heart
Disease
The association of LTL with incident CHD was significantly reduced after adjusting
for covariates, and the nonlinearity was no longer statistically significant (p > 0.05) (Figure
Figure 3.
Generalized additive model (GAM) plots of partial effects of pollutant concentrations on
leukocyte telomere length (LTL) adjusting for covariates (age, sex, ethnicity, education, BMI, smoking
status, alcohol intake frequency, physical activity, percent of greenspace in 1000 m buffers). The tick
marks on the x-axis are z-scores of the concentration of an air pollutant. The y-axis represents the
partial effect of the concentration of an air pollutant. The areas between dashed lines indicate the 95%
confidence intervals. (
a
) NO
2
in 2010 and LTL; (
b
) NO
2
average 2005–2007 and LTL; (
c
) NO
x
in 2010
and LTL; (
d
) PM
2.5
in 2010 and LTL; (
e
) PM
2.5
absorbance in 2010 and LTL; (
f
) PM
2.5–10
in 2010 and
LTL; (g) PM10 in 2007 and LTL; (h) PM10 in 2010 and LTL.
3.3. Associations of Leukocyte Telomere Length and Air Pollutants with Incident Coronary
Heart Disease
The association of LTL with incident CHD was significantly reduced after adjusting for
covariates, and the nonlinearity was no longer statistically significant (p> 0.05) (Figure 4a).
Assuming a linear relationship between LTL and incident CHD in a Cox proportional
Toxics 2023,11, 489 8 of 14
hazards model, the adjusted hazard ratio (HR) of incident CHD per SD increase in LTL
was 0.95 (95% CI 0.94 to 0.96, p< 0.001). Without the linearity assumption, the adjusted
HR comparing a given z-score to the mean z-score (=0) of LTL is presented in Figure 4. For
example, the adjusted HR comparing z = 2 to z = 0 was 0.94 (95% CI 0.89 to 0.99) versus
1.12 (95% CI 1.07 to 1.16) comparing z =
−
2 to z = 0 (Figure 4a). The selected z-scores
were the observed z-scores closest to
−
3,
−
2,
−
1, 1, 2, and 3 for LTL and for individual
air pollutants.
Toxics 2023, 11, x FOR PEER REVIEW 10 of 16
Figure 4. Associations of pollutant concentrations or leukocyte telomere length (LTL) with incident
coronary heart disease (CHD), with or without adjustment for covariates. The tick marks on the x-
axis are z-scores of leukocyte telomere length or the pollutant concentration. The y-axis represents
the hazard ratio of incident coronary heart disease, comparing a given z-score to the mean z-score
(=0) of the x-axis variable. The areas between the dashed lines indicate the 95% confidence intervals.
Adjusted results are in blue versus the unadjusted results in red. (a) LTL and incident CHD; (b) NO2
in 2010 and incident CHD; (c) NO2 average 2005–2007 and incident CHD; (d) NOx in 2010 and inci-
dent CHD; (e) PM2.5 in 2010 and incident CHD; (f) PM2.5 absorbance in 2010 and incident CHD; (g)
PM2.5–10 in 2010 and incident CHD; (h) PM10 in 2007 and incident CHD; (i) PM10 in 2010 and incident
CHD.
3.4. Does LTL Mediate the Associations between Air Pollutants and Incident Heart Disease?
Figure 4.
Associations of pollutant concentrations or leukocyte telomere length (LTL) with incident
coronary heart disease (CHD), with or without adjustment for covariates. The tick marks on the
x-axis are z-scores of leukocyte telomere length or the pollutant concentration. The y-axis represents
the hazard ratio of incident coronary heart disease, comparing a given z-score to the mean z-score
(=0) of the x-axis variable. The areas between the dashed lines indicate the 95% confidence intervals.
Adjusted results are in blue versus the unadjusted results in red. (
a
) LTL and incident CHD;
(b) NO2
in 2010 and incident CHD; (
c
) NO
2
average 2005–2007 and incident CHD; (
d
) NO
x
in 2010 and
incident CHD; (
e
) PM
2.5
in 2010 and incident CHD; (
f
) PM
2.5
absorbance in 2010 and incident CHD;
(
g
) PM
2.5–10
in 2010 and incident CHD; (
h
) PM
10
in 2007 and incident CHD; (
i
) PM
10
in 2010 and
incident CHD.
Toxics 2023,11, 489 9 of 14
In contrast, both unadjusted and adjusted associations of pollutant concentrations
with incident CHD were similar (Figure 4). Pollutant concentrations in the lower range
were positively associated with the risk of incident CHD (excluding PM
2.5–10
and PM
10
).
The risk of developing CHD increased when the pollutant concentration was above the
average, but the risk dropped after the concentration reached a critical level, which varied
with air pollutants, e.g., z-score 2 for NO
x
, 3 for PM
2.5
, and 1 for NO
2
(Figure 4). However,
the associations of extremely high pollutant concentrations with incident CHD came with
great uncertainty.
3.4. Does LTL Mediate the Associations between Air Pollutants and Incident Heart Disease?
If the associations of air pollutants with incident CHD are mediated by LTL, we would
expect that adjusting the associations for LTL would attenuate them significantly. The
mediating role of LTL, however, was not supported by our results. We found similar
hazard ratios for incident CHD when comparing a z-score to the mean z-score (=0) of the
concentration of an air pollutant from the models adjusting for covariates only and for
covariates plus LTL (assuming a linear relationship with incident CHD, since non-linearity
was not significant, p= 0.380 (Figure 4)) (Figure 5).
Toxics 2023, 11, x FOR PEER REVIEW 11 of 16
If the associations of air pollutants with incident CHD are mediated by LTL, we
would expect that adjusting the associations for LTL would aenuate them significantly.
The mediating role of LTL, however, was not supported by our results. We found similar
hazard ratios for incident CHD when comparing a z-score to the mean z-score (=0) of the
concentration of an air pollutant from the models adjusting for covariates only and for
covariates plus LTL (assuming a linear relationship with incident CHD, since non-
linearity was not significant, p = 0.380 (Figure 4)) (Figure 5).
Figure 5. Association of the concentration of an air pollutant with incident coronary heart disease
adjusting for covariates only or covariates plus leukocyte telomere length. The tick marks on the x-
axis are z-scores of the concentration of an air pollutant. The y-axis represents the hazard ratio of
incident coronary heart disease comparing a given z-score to the mean z-score (=0) of the x-axis
variable. The areas between dash lines indicate the 95% confidence intervals. Results adjusting for
Figure 5. Association of the concentration of an air pollutant with incident coronary heart disease
Toxics 2023,11, 489 10 of 14
adjusting for covariates only or covariates plus leukocyte telomere length. The tick marks on the
x-axis are z-scores of the concentration of an air pollutant. The y-axis represents the hazard ratio
of incident coronary heart disease comparing a given z-score to the mean z-score (=0) of the x-axis
variable. The areas between dash lines indicate the 95% confidence intervals. Results adjusting for
covariates only are in blue versus the results in red adjusting for covariates and leukocyte telomere
length (which assumed a linear relationship in the model due to insignificant non-linearity p= 0.380
(Figure 4)). (
a
) NO
2
in 2010 and incident CHD; (
b
) NO
2
average 2005–2007 and incident CHD;
(c) NOx
in 2010 and incident CHD; (
d
) PM
2.5
in 2010 and incident CHD; (
e
) PM
2.5
absorbance in
2010 and incident CHD; (
f
) PM
2.5–10
in 2010 and incident CHD; (
g
) PM
10
in 2007 and incident CHD;
(h) PM10 in 2010 and incident CHD.
Next, we considered the air pollutants that showed inverse associations with LTL
and CHD at the lower range of their concentrations, i.e., NO
2
, NO
x
, PM
2.5
, and PM
2.5
absorbance. We conducted an analysis to estimate the mediation effect of LTL on the
association between pollutant concentrations in the lower range and the development
of CHD. The proportion of effect on CHD mediated by LTL comparing a lower range of
pollutant concentration to the range centered at the mean was less than 3% across the
air pollutants (Tables S2–S6), providing evidence against the mediating role of LTL in
the association.
4. Discussion
While we did not find consistent and monotonic associations of air pollutants with
CHD across the entire concentration range of pollutants, we did find associations in the
lower concentration range for several pollutants. Pollutant concentrations in the lower
range were decreasingly associated with longer LTL and a lower risk of CHD. Additionally,
longer LTL was associated with a lower risk of CHD, which is consistent with the prior
literature [13–15].
The primary innovation of our analysis was to test whether LTL mediated the associa-
tion between air pollution exposure and incident CHD. There is considerable evidence that
air pollution drives adverse cardiovascular health outcomes through inflammation [
6
,
7
].
To our knowledge, there has been little research on the possible role of telomere length
as a biological pathway from air pollution exposure to CHD. This pathway is plausible
since LTL has been linked to inflammation [
38
] and air pollution is also associated with
inflammatory responses [6].
The secondary innovation of our analysis was to adopt non-linear modeling that
allows the exposure–outcome relationship to vary with the exposure level. We found
monotone associations of air pollution exposure with LTL and CHD only in the lower
range of pollutant concentrations. In contrast, the associations in the higher range of
pollutant concentration were inconclusive due to greater uncertainty, which will require
further investigation. Contradictory to our findings, one study [
22
] showed no significant
association between air pollution exposure and LTL using the same data source. The
inconsistency may be partly explained by modeling differences. Notably, they assessed
non-linearity via a quadratic term and found no strong evidence across pollutants.
The evidence from our mediation analysis does not support the hypothesis that
LTL is on the biological pathway between air pollution exposure and CHD. LTL and
exposure to air pollution were independently associated with CHD across the whole
range of pollutant concentration. The mediated effects of LTL in the associations between
pollutant concentrations in the lower range (z scores in (
−
Inf,
−
2], (
−
2,
−
1], or (
−
1,
−
0.5]
versus z scores in (
−
0.5, 0.5]) and CHD were all less than 3% across the pollutants we
analyzed. Thus, we would suggest that it is more likely that air pollution in the lower range
influences CHD through pathways that do not involve LTL. Future studies may address
both mediation and moderation effects of telomere length on acute cardiovascular events
in addition to CHD.
Toxics 2023,11, 489 11 of 14
Our analysis has several strengths. First, the prospective study design ensured the
temporality of the effects of air pollution exposure and LTL on incident CHD. Second, we
modeled the whole range of a form of exposure (e.g., exposure to air pollution) to determine
the effect on an outcome (e.g., LTL) without a presumption of a linear relationship. Third,
the attrition rate was low, as participants were followed up for health outcomes through
electronic linkages. Fourth, the air pollution models have good accuracy for estimating
ambient concentrations at residences, albeit more so for PM
2.5
than PM
10
or NO
2
. Fifth,
multiple air pollutants were included, which is a priority in present day environmental
epidemiology research. Finally, the sample size was large, providing substantial statistical
power, and the LTL data were collected through rigorous methods with high levels of
quality control.
Despite these considerable strengths, there are some limitations to the source data
we used. First, telomeres from cardiac tissues may be more relevant to this study than
peripheral blood leukocytes, but the correlations between telomere length among different
tissues are generally positive [
39
]. Moreover, data on LTL over time are not available for
study of the mediating effect of telomere attrition in the association between air pollution
exposure and incident CHD. Second, ambient concentrations of pollutants in the homes of
study participants are widely used; however, this estimation of exposure likely contains
errors because of self-reported addresses, errors from matching in geographic information
systems, and differences indoors and the movement of participants to other locations. We
have less confidence in the PM
10
data because they varied considerably by year. Exposure
misclassification would likely bias effect estimates toward the null [
40
]. Third, we did not
consider other potential mediators, likely also related to inflammation but independent
of LTL to CHD. Although we included many possible confounders as co-variates, there
remains the possibility our analysis does not meet the assumptions of no unmeasured
confounding for the mediation analysis. However, the associations of air pollutants with
incident CHD minimally changed after adjusting for LTL, suggesting no mediating effect
of LTL. Fourth, variables such as the UKB baseline assessment center and Townsend
deprivation index were measured by area, and they more or less overlapped with the
ambient pollutant concentrations, so they were not adjusted in statistical models. However,
we used individual data of education, which can serve as a proxy for socio-economic
status. Fifth, the sample size was significantly reduced for extreme telomere length and
pollutant concentrations, which come with greater uncertainty in estimation and restrict
additional analyses to separate populations with differential vulnerability to air pollution
exposure, e.g., women versus men and older adults versus younger adults [
41
]. Finally,
generalizability is reduced by the limited racial/ethnic composition of the study population
and its geographic location in the UK.
5. Conclusions
In a large, well-defined health cohort, we confirmed the association between shorter
LTL and higher risk of CHD. Our analysis showed non-linear relationships of exposure
to air pollution with LTL and CHD. Specifically, increased exposure to air pollution in
the lower range was associated with shorter LTL and higher risk of CHD. The association
between air pollution exposure and CHD, however, was not mediated by LTL. Our findings
suggest that it is more likely that air pollution in the lower range influences CHD through
pathways that do not involve LTL. An area for future research would be to improve
exposure assignment of air pollution to get closer to actual personal exposure.
Supplementary Materials:
The following supporting information can be downloaded at: https://www.
mdpi.com/article/10.3390/toxics11060489/s1, Table S1: UK Biobank field IDs to extract data; Table S2:
Casual mediation analysis to estimate average causal mediation effects (ACMEs) through leukocyte
telomere length and average direct effects (ADE) of NO
2
concentrations in 2010 in the lower range vs.
the average on the risk of incident coronary heart disease; Table S3: Casual mediation analysis to
estimate average causal mediation effects (ACMEs) through leukocyte telomere length and average
direct effects (ADE) of NO
2
concentration averages from 2005 to 2007 in the lower range vs. the
Toxics 2023,11, 489 12 of 14
average on the risk of incident coronary heart disease; Table S4: Casual mediation analysis to estimate
average causal mediation effects (ACMEs) through leukocyte telomere length and average direct
effects (ADE) of NO
x
concentrations in 2010 in the lower range vs. the average on the risk of
incident coronary heart disease; Table S5: Casual mediation analysis to estimate average causal
mediation effects (ACMEs) through leukocyte telomere length and average direct effects (ADE) of
PM
2.5
concentrations in 2010 in the lower range vs. the average on the risk of incident coronary
heart disease; Table S6: Casual mediation analysis to estimate average causal mediation effects
(ACMEs) through leukocyte telomere length and average direct effects (ADE) of PM
2.5
absorbance
concentrations in 2010 in the lower range vs. the average on the risk of incident coronary heart
disease; Figure S1: Histograms of leukocyte telomere length and air pollutants; Figure S2: Spearman
correlations between pollutant concentrations; Figure S3: Generalized additive model (GAM) plots of
the partial effects of air pollutants on leukocyte telomere length, with no adjustment for covariates.
The tick marks on the x-axis are z-scores of an air pollutant. The y-axis represents the partial effect
of each air pollutant. The areas between dash lines indicate the 95% confidence intervals. (a) NO
2
2010 and LTL; (b) NO
2
average 2005–2007 and LTL; (c) NO
x
2010 and LTL; (d) PM
2.5
2010 and LTL;
(e) PM2.5 absorbance and LTL
; (f) PM
2.5–10
2010 and LTL; (g) PM
10
2007 and LTL; (h) PM
10
2010
and LTL.
Author Contributions:
Conceptualization, C.-L.K., R.L. and D.B.; methodology, C.-L.K., R.L., L.d.C.G.
and D.B.; analysis, C.-L.K. and L.d.C.G.; writing—original draft preparation, C.-L.K. and D.B.;
writing—review and editing, C.-L.K., R.L., L.d.C.G. and D.B.; funding acquisition, C.-L.K. and D.B.
All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the National Institute of Nursing Research, National Institutes
of Health, USA (R21NR018963-01A1). C.L.K. and R.H.F. were supported by the Claude D. Pepper
Older American Independence Centers (OAIC) program: P30AG067988. D.B. was supported by
National Institute of Environmental Health Sciences, R01ES030289.
Institutional Review Board Statement:
The UKB received ethical approval from the National Re-
search Ethics Service Committee North West–Haydock (reference 11/NW/0382).
Informed Consent Statement:
Informed consent of all UKB participants was obtained by UKB for
their data to be used in health-related research.
Data Availability Statement: The UKB data are available upon approved request.
Acknowledgments:
Access to UK Biobank data was granted under application no. 92647. This study
was conducted under the UKB application number 92647 (https://www.ukbiobank.ac.uk/enable-
your-research/approved-research/research-to-inform- the-field- of-precision-gerontology, accessed
on 22 May 2023). This research used data assets made available by National Safe Haven as part of the
Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the
Office for National Statistics and funded by UK Research and Innovation (research which commenced
between 1 October 2020–31 March 2021 grant ref MC_PC_20029; 1 April 2021–30 September 2022
grant ref MC_PC_20058). This research also used data provided by patients and collected by the NHS
as part of their care and support. Copyright
©
(year), NHS England. Re-used with the permission of
the NHS England [and/or UK Biobank]. All rights reserved.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
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