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Association of traffic-related air pollution and lens opacities in the Los Angeles Latino Eye Study

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Association of traffic-related air pollution and lens opacities in the Los Angeles Latino Eye Study

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ASSOCIATION OF TRAFFIC-RELATED AIR POLLUTION AND LENS OPACITIES
IN THE LOS ANGELES LATINO EYE STUDY

By

Yi Chen

A Thesis Presented to the
SCHOOL OF PREVENTIVE MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA

In Partial Fulfillment of the Requirement for
the Degree of Master of Science
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)

Under the Supervision of Dr. Farzana Choudhury

May 2018
ii

Acknowledgements

I would like to express heartfelt thanks to the chair of my thesis committee, Dr. Farzana
Choudhury for her kind help and advices during directing my research. I am particularly
grateful to Dr. Towhid Salam, who provided me with timely help and valuable suggestions
regarding data analysis and manuscript preparation. I’m thankful to Dr. Choudhury and Dr.
Salam for providing the data for this analysis. I also appreciate Dr. Christiane Lane’s help
and support for my thesis. Special thanks to my family and friends for their support.

iii

Table of Contents

Acknowledgements ............................................................................................................. ii
List of Figures and Tables.................................................................................................. iv
Abstract ............................................................................................................................... v
Introduction and Background ............................................................................................. 1
Methods............................................................................................................................... 4
Study design ................................................................................................................................ 4
Sample ......................................................................................................................................... 5
Study variables ............................................................................................................................ 5
Traffic exposures ..................................................................................................................... 5
Outcome measures................................................................................................................... 5
Other risk factors ..................................................................................................................... 6
Statistical analysis ....................................................................................................................... 7
Results ................................................................................................................................. 8
Study Cohort................................................................................................................................ 8
Comparison of Participants’ Baseline Characteristics by Lens Opacity Status: ......................... 8
Sociodemographic indicators and Lens Opacity ..................................................................... 8
Clinical indicators and Lens Opacity....................................................................................... 9
Traffic-related air pollution indicators and Lens Opacity ..................................................... 12
Univariate Associations of TRAP and lens opacities ................................................................ 13
Multivariate Associations of TRAP and lens opacities ............................................................. 16
Discussion ......................................................................................................................... 21
Conclusion ........................................................................................................................ 25
References ......................................................................................................................... 27

iv

List of Figures and Tables

Figure 1. Flow-chart of analysis cohort .............................................................................10
Table 1. Distribution of Sociodemographic and Clinical Risk Indicators Stratified by
Category of Lens Opacity ..................................................................................................11
Table 2. Distribution Summaries for Traffic Related Exposures Metrics .........................12
Table 3. Spearman's Correlation Matrix of Traffic Related Exposures ...........................13
Table 4. Univariate Associations of Traffic-Related Air Pollution and Categories of Lens
Opacity ...............................................................................................................................15
Table 5.1. Multivariate Associations of Traffic-Related Air Pollution and All Lens
Changes ..............................................................................................................................17
Table 5.2. Multivariate Associations of Traffic-Related Air Pollution and Any Type of
Lens Opacity ......................................................................................................................18
Table 5.3. Multivariate Associations of Traffic-Related Air Pollution and Cortical-Only
Lens Opacity ......................................................................................................................19
Table 5.4. Multivariate Associations of Traffic-Related Air Pollution and Nuclear-Only
Lens Opacity ......................................................................................................................20

v

Abstract
In epidemiologic studies, exposures to tobacco smoke and indoor cooking have been
associated with lens opacity and oxidative stress has been implicated in these associations.
Similarly, in urban population, traffic related air pollution (TRAP) has been associated with
such oxidative damages to multiple tissues. We hypothesized that exposure to TRAP is
associated with development of lens opacities. We tested this hypothesis by using data from
the Los Angeles Latino Eye Study (LALES), a population-based study among Latinos who
were 40 years or older at study entry. Geographical Information System was used to
estimate TRAP by using two surrogate measures of exposures ：distance to the nearest
highways/freeways from participants’ home and total road lengths within 50m, 100m,
200m, and 300m circular buffers around home. We used multiple logistic regressions to
investigate the associations of these TRAP metrics on prevalence of different types of lens
opacities (all lens changes, any lens opacity, cortical-only lens opacity and nuclear-only
lens opacity) in the LALES in 6,141 participants. In final multivariate model, we adjusted
for age, income, body mass index, myopia, history of diabetes, systolic blood pressure and
smoking status. In multivariate analysis, we found statistically significant associations with
total road lengths around home and nuclear-only type of lens opacity but not with other
types (any lens opacity and cortical-only). One standard deviation (SD) (372.11m) higher
in total road length within 200m buffer around home was associated with 20% increase in
prevalence of nuclear-only lens opacity [95% Confidence interval (95% CI) in odds ratio
vi

(OR) = (1.01- 1.41)]. Similar associations were observed with road lengths within 50m and
100m buffers as well; however, these associations were marginally significant [95% CI in
OR = 1.15 (0.98- 1.35) for both]. Distance to nearest highway/freeway was not associated
with lens opacity. Our results indicate that TRAP is associated with nuclear-only lens
opacity in Latinos.
1

Introduction and Background
Visual impairment and blindness can substantially compromise an individual’s
quality of life and be detrimental to their mental and physical well-being.
1
The prevalence
of visual impairment is increasing in the United States with the aging of the population,
and is projected to more than double from 3.22 million in 2015 to 6.95 million in 2050.
Cataract or lens opacity is the major cause of visual impairment and blindness in the United
States as well as in the world. Cataracts are opacities in the lens of the eye and its capsule.
The common symptoms of lens opacity include blurry vision, night blindness, dull or
yellow vision, and double vision. It is estimated that cataracts affect nearly 22 million
Americans age 40 and older; and by age 65, more than half of all Americans have diagnosis
of cataracts.
2
The direct healthcare costs for cataract treatment in the US are high, reaching
approximately $6.8 billion per year. Furthermore, un-operated lens opacities significantly
lower health-related quality of life by causing substantial visual and functional disability.
3-
5
For these reasons, understanding risk factors for development of lens opacities is a priority
in public health.
The pathobiology of lens opacity is still poorly understood. The development of
lens opacities is often considered a normal part of the aging process. Results from previous
epidemiologic studies suggest age, family history of cataract, diabetes and smoking are
most consistent risk factors that have significant effects on cataract development.
6-8
The
association between smoking and lens opacity has been observed and reported in some
2

studies. The impact of smoking has been most consistently observed on nuclear sclerosis
and sometimes with posterior subcapsular types,
9-15
and is most likely be due to the
oxidative stress caused by cigarette smoke.
16,17
A leading cause to lens opacity is oxidative
damage to the sulfhydryl proteins in lens, which plays important roles in both structural
and functional support in crystalline lens.
18
The high amount of molecular aggregates of
crystalline protein fractions in cells is an important intracellular evidence for lens opacity.
Lipid peroxidation and free-radical oxidative injury are also implicated in the development
of lens opacities.
19

While majority of ocular epidemiology studies were conducted in Caucasians in
USA, Europe and Australia,
7
there had been paucity of studies in other racial/ethnic groups.
In the United States, Latino population is the largest and fastest growing racial/ethnic group
comprising of 16.3% of the total U.S. population in the 2010 census.
20
The Los Angeles
Latino Eye Study (LALES) was conducted to examine the prevalence and identify risk
factors of various ocular diseases including lens opacity among Latinos. LALES is a 5-
year (from February 2000 to May 2003) population-based longitudinal study in Latinos,
aged 40 years and older, living in 6 census tracts in the city of La Puente, Los Angeles
County.
21
In LALES, we have also previously reported that smoking is associated with
nuclear lens opacity but not with other types of lens opacities.
7

In addition to tobacco smoke, air pollution also imparts oxidative and nitrosative
stress which could be associated with development of lens opacity. In some developing
3

nations (Nepal and India), exposures to air pollution from indoor cooking has been
associated with increased risk of cataracts in women.
22
This association was especially
stronger between biomass fuels and nuclear cataract.
23
In urban populations, traffic-related
air pollution (TRAP) is a major contributor to air pollution, however, the role of TRAP on
lens opacity development has not been reported to date. Previous studies have
demonstrated that residential exposure to TRAP has negative effects on lung functions in
children between 10-18 years of age,
24
and higher exhaled nitric oxide level, which is a
noninvasive marker of airway inflammation.
25
Since both lungs and eyes are exposed to
the air, oxidative stress from TRAP exposure is likely to affect the eyes as well. Exposure
to ambient air derived from TRAP was positively associated with ocular surface
abnormalities by affecting gene expression, such as increase in mucin 5AC mRNA levels
and goblet cells density on the conjunctiva.
26

Given there is evidence from epidemiological studies that tobacco smoke and
indoor cooking are associated with increased risk of lens opacity, particularly the nuclear-
only lens opacity, we hypothesized that residential-level exposures to TRAP is associated
with lens opacity, and this association is stronger for nuclear-only lens opacity. We tested
this hypothesis among participants from LALES, a population-based study of ocular
disease among Latinos living in Los Angeles.

4

Methods
This study used the socio-demographic, clinical and ocular clinical data collected
from interview and in-clinic medical and ocular examination of the Los Angeles Latino
Eye Study (LALES). Based on these data, we have conducted a cross-sectional study to
investigate association between traffic-related air pollution and prevalence of lens opacity
in Latinos older than 40 years old in Los Angeles County.
Study design
The Los Angeles Latino Eye Study (LALES) is a population-based longitudinal
study of eye disease in Latinos, aged 40 years and older, living in 6 census tracts in the city
of La Puente, Los Angeles County. The detailed methods have been described in detail
previously.
21
In short, 6 census tracts of La Puente were chosen largely because the dense
population of Hispanics living there. Self-identified Latinos were screened for eligibility
based on their age and residency. Ethical approval for the study was obtained from Los
Angeles County/University of Southern California institutional Review Board (Proposal
#HS-969044).
In-home interviews were conducted after obtaining informed consent. At cohort
entry, socio-demographic, clinical and ocular clinical data were collected from interview
and in-clinic medical and ocular examination. Trained ophthalmologists and technicians
performed a comprehensive ocular examination using standardized protocols.
5

Sample
Of the 6,357 eligible Latinos who completed ophthalmic examinations of LALES,
6,142 had Lens Opacities Classification System II (LOCS II) grade data. One participant
was excluded due to lack of TRAP estimate.
Study variables
Traffic exposures
Two metrics of TRAP were used: distance to the nearest highway/freeway from
each participant’s home and the total road lengths within 50m, 100m, 200m, and 300m
circular buffers centered at each participant’s home. The residential addresses of
participants were geocoded. The distances to nearest highway/freeway were calculated and
total road length within different buffers around participants’ residences were calculated
using ArcGIS version 10.1. All TRAP variables were scaled by dividing with their standard
deviations in the analyses for standardization to equalize the data variability.
Outcome measures
Lens opacities were defined by the Lens Opacities Classification System II (LOCS
II) grades. Lens opacities were graded into five levels of severity for nuclear and five
posterior subcapsular (PSC) and seven for cortical opacity.
Cortical/nuclear-only lens opacity: If a participant had one type of lens opacity with
LOCS II score greater than 2, then this participant was classified and considered to have
that type of lens opacity (nuclear-only, cortical-only or PSC-only).
27

6

Any lens opacity: If a participant had a definitive diagnosis of one of the major
types (nuclear, cortical and PSC opacities), they were categorized into “any lens opacity”
group. These participants might also have a different type of lens opacity in the other eye.
All lens changes: If one participant had “any lens opacity” defined above, or he/she
had a history of previous surgery, including presence of aphakia (no lens) or pseudophakia
(prosthetic intraocular lens), or the cataract is too advance to grade, then they were included
into the “all lens changes” group.
7

Other risk factors
Potential sociodemographic risk factors included age, gender, smoking status, and
annual household income. Potential clinical risk factors included BMI, myopia, history of
diabetes, hypertension, and presence of large-drusen. For smoking, participants were
categorized into three groups, non-smoker, ex–smoker and current-smoker. Ex-smokers
were participants who used to smoke, but stopped smoking before cohort entry.
Socioeconomic status was assessed using annual household income. In LALES data, 52%
of participants had an annual household income less than $20,000. So income was
dichotomized into two groups: those with annual income <$20,000 and those with income
≥$20,000.
7,21
BMI was categorized into normal, overweight (BMI between 25 and 29.9)
and obese (BMI of 30 and above) based on conventional classification. Systolic blood
pressure above 130mm Hg was used to define hypertension.
28
Presence of large drusen was
7

defined as participants who had macula with approximately one-twelfth disc diameter of
the average disc diameter, which is approximately 125μm.
29

Statistical analysis
Differences in distributions of sociodemographic and clinical risk factors between
participants with and without lens opacities were examined using independent t-tests and
Pearson chi-square tests for continuous and categorical variables, respectively.
Correlations among TRAP metrics were examined using Spearman rank correlations to
avoid collinearity. Logistic regression analyses were utilized to assess both univariate and
multivariate associations between TRAP and prevalence of lens opacities. For multivariate
associations, we adjusted for potential confounders by successively adding more a priori
covariates to previous models We first assessed the age adjusted association between
TRAP and different kinds of lens opacities (model 1), then added annual household income
as the sociodemographic factor to model 1 (model 2). We then added clinical indicators of
BMI, history of myopia, diabetes, hypertension and large drusen to model 2 (model 3). In
the final model, we added smoking status [non-smoker, ex-smoker and current smoker] to
model 3 (model 4). Models were built in this order to examine different categories of
variables for their impact of adjustment in sequence. Missing indicators were used in the
analyses for covariates with missing data. All tests were two-sided at a 5% significance
level. All statistical analyses were performed using SAS software v. 9.4 (SAS Institute,
Inc., Cary, NC, USA).

8

Results
Study Cohort
The final cohort of analysis consisted of 6,141 participants with non-missing values
of the key ocular and environmental variables. The age distribution of this cohort was more
or less similar to the age-distribution of Latinos from the US census data of 2010.
21
Figure
1 is a flowchart of the study cohort in this analysis. Of the 6,141 participants, 4,899 of them
were with no lens opacity, 1,242 participants were with all lens changes and 1,076
participants were with any kinds of lens opacity. For certain types of lens opacity, 468
participants carried cortical-only lens opacity and 217 participants carried nuclear-only
lens opacity. There were only 27 participants with PSC type of lens opacities. The
relatively small sample size precluded analysis for testing association of TRAP and PSC
type.
Comparison of Participants’ Baseline Characteristics by Lens Opacity Status:
Sociodemographic indicators and Lens Opacity
The distributions of sociodemographic and clinical risk factors by different
categories of lens opacities are presented in Table 1. Participants with lens changes and
opacities were about 10 – 18 years older in average age compared to those without lens
opacity (all p-values < 0.0001). There was no statistically significant difference in gender
distribution between participants with and without lens opacities (all p-values > 0.05). The
proportions of current smokers were statistically significantly higher for participants all
9

lens change and any lens opacities compared with those with no lens opacity (p-values =
0.003 and 0.01, respectively). Participants with lens opacities were more likely to have
lower household income when compared with those without lens opacity, these differences
were statistically significant (all p-values < 0.0001).
Clinical indicators and Lens Opacity
Participants with lens opacities were more likely to have history of diabetes,
hypertension and large drusen compared with those who had no lens opacity, these
differences are statistically significant (all p < 0.0001). Compared to participants with no
lens opacity, those with lens opacities were more likely to have myopia (all p <0.01), except
cortical only type of lens opacity (p = 0.07). Participants with lens opacities were more
likely to be of normal weight when compared with those with no lens opacity in our study
cohort (all p < 0.05).

10

Figure 1. Flow-chart of analysis cohort

Participants examined at
baseline (n =6357)

(n = 6357)
Participants don’t have both
environmental information and
LOP information (n =1)

Participants merged by both
environmental and ocular data
(n = 6141)
Participants with
no lens opacity
(n=4899)
[control group]

Participants with all lens
changes (n = 1242)
Participants with any lens
opacity (n = 1076)
Participants with
cortical-only lens
opacity (n = 468)
Participants with
nuclear-only lens
opacity (n = 217)
Participants with
PSC-only lens
opacity (n = 27)
Participant with LOCS II data
(n =6142)

(n = 6357)
11

Table 1. Distribution of Sociodemographic and Clinical Risk Indicators Stratified by Category of Lens
Opacity
Risk Indicators
No lens
opacity
(n=4899)
All lens
changes
(n=1242)
Any lens
opacity
(n=1076)
Cortical-
only lens
opacity
(n=468)
Nuclear-
only lens
opacity
(n=217)
Sociodemographic indicators
Age [mean (SD)] 51.7 (8.7)
67.3
(10.2)**
66.6
(10.0)**
62.4
(8.6)**
69.4
(8.8)**
Gender(male) 41.9% 40.7% 40.2% 42.1% 40.6%
Smoking status§
Non-smoker 62.0% 60.2%** 60.2%* 60.5% 58.1 %
Ex-smoker 23.6% 27.9% 27.6% 26.6% 25.6%
Current smoker 14.3% 12.0% 12.2% 12.9% 16.3%
Annual Household
Income < $20,000
47.5% 66.3%** 66.7%** 64.8%** 69.4%**
Clinical indicators
Myopia 42.9% 53.8%** 48.5%** 38.3% 55.5%**
History of diabetes 13.7% 31.2%** 30.5%** 29.7%** 25.0%**
Body mass index§
Normal 10.3% 15.9%** 15.0%** 14.1 %* 16.7%**
Overweight 39.1 % 37.8% 38.1% 36.3% 40.7 %
Obese 50.6% 46.3 % 47.0% 49.7% 42.7 %
Hypertension 25.9% 50.4%** 49.7%** 45.1%** 47.9%**
Presence of large drusen 12.1% 21.8%** 21.1 %** 19.1%** 23.3%**
1) Any lens opacity includes any nuclear, cortical or PSC lens opacities.
All lens changes include any nuclear, cortical or PSC lens opacities, too advanced to
grade (hyper mature cataract); and history of previous surgery.
2)SD = standard deviation; * stands for p-values < 0.05, ** stands for p-value < 0.01.
3)Difference of risk indicators in each category of lens opacity compared to no lens opacity group were
analyzed by t-test for continuous variables and chi-square test for categorical variables. p-values < 0.05
were considered statistically significant. Numbers do not always add up because of missing data.
4)§ P-values testing overall association of the variable with each lens opacity category.
12

Traffic-related air pollution indicators and Lens Opacity
The distributions of TRAP exposure metric are summarized in Table 2, the
distribution of distance to nearest highway was right-skewed and the total road length in
different buffers (50m, 100m, 200m, 300m) were normally distributed. In general, average
distance from a nearest highway/freeway was about 2km. Correlations among residential
distance to nearest highway/freeway and total road length in different buffers were weak
(Spearman correlation coefficients ranged from 0.10 to 0.21). The correlations among total
road length within different buffers were from low to moderate (Spearman correlation
coefficients ranged 0.14 to 0.62, Table 3).

Table 2. Distribution Summaries for Traffic Related Exposures Metrics
Variable
Mean
(SD)
Percentile
5th 25th 50th 75th 95th
Distance to nearest
highway/freeway
(meter)
2102.81
( 874.78 )
529.11 1462.93 2352.32 2779.23 3290.6
Total road length in 50m
buffer around home
(meter)
127.9
( 48.19 )
76.16 97.86 114.27 155.67 209.51
Total road length in 100m
buffer around home
(meter)
484.86
( 135.09 )
273.59 402.23 483.24 573.68 701.00
Total road length in 200m
buffer around home
(meter)
1869.08
( 372.11 )
1217.69 1615.36 1887.33 2140.02 2436.71
Total road length in 300m
buffer around home
(meter)
2881.56
( 1456.59 )
528.64 1542.81 3095.52 4093.93 4938.21
1) SD = standard deviation

13

Table 3. Spearman's Correlation Matrix of Traffic Related Exposures
Variables 1) 2) 3) 4)
1) Distance to nearest highway/freeway (meter) 1

2) Total road length in 50m buffer around home 0.11** 1

3) Total road length in 100m buffer around home 0.10** 0.42** 1

4) Total road length in 200m buffer around home 0.10** 0.14** 0.62** 1
5) Total road length in 300m buffer around home 0.21** 0.19** 0.25** 0.32**
1) ** P<0.01

Univariate Associations of TRAP and lens opacities
The univariate associations between TRAP indicators and the lens opacities are
summarized in Table 4. We did not observe any statistically significant association
between distance to nearest highway/freeway from participants’ homes and any lens
changes or opacities. In contrast, several metrics of the total road lengths within different
buffers showed statistically significant associations with the outcomes. For all lens changes
and lens opacities, we found that total road lengths within 200m buffer was consistently
associated with statistically significant higher odds of all outcomes and but not with the
other road length metrics. One standard deviation higher total road length within 200m
buffer around home was associated with 1.14 times higher prevalence of all lens changes
and any lens opacities [(95% CI = (1.07- 1.22) for both types)] and 1.13 [95% CI = (1.02-
1.24)] times higher prevalence of cortical-only lens opacity and 1.30 times [95% CI =
(1.13-1.49)] higher prevalence for nuclear-only lens opacity. For total road length within
14

other buffers, one SD higher in total road length within 50m, 100m, and 300m buffer
around home were associated with 1.12, 1.16, and 1.14 times [95% CI = (0.99-1.28), (1.01-
1.32), and (0.99-1.30) respectively] higher prevalence of nuclear-only lens opacity,
respectively. While the ORs for nuclear-only lens opacity with 100m and 200m buffers
were statistically significant, the corresponding ORs for 50m and 300m buffers were
marginally significant (p = 0.08 and 0.07, respectively).
15

Table 4. Univariate Associations of Traffic-Related Air Pollution and Categories of Lens Opacity
Traffic related exposures
All lens
changes
Any lens
opacity
Cortical-only
lens opacity
Nuclear-only
lens opacity
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
Distance to nearest
highway/freeway
0.99
(0.93- 1.05)
0.99
(0.93- 1.06)
1.00
(0.91- 1.10)
0.99
(0.86- 1.13)
Total road length in 50m
buffer around home
1.00
(0.94- 1.06)
0.99
(0.93- 1.06)
0.93
(0.85- 1.03)
1.12
(0.99- 1.28)
Total road length in 100m
buffer around home
1.03
(0.97- 1.10)
1.03
(0.97- 1.10)
0.99
(0.90- 1.08)
1.16
(1.01- 1.32)*
Total road length in 200m
buffer around home
1.14
(1.07- 1.22)**
1.14
(1.07- 1.22)**
1.13
(1.02- 1.24)*
1.30
(1.13- 1.49)**
Total road length in 300m
buffer around home
1.01
(0.95- 1.08)
1.02
(0.95- 1.09)
1.01
(0.91- 1.10)
1.14
(0.99- 1.30)
1) Any lens opacity includes any nuclear, cortical or PSC lens opacities.
All lens changes include any nuclear, cortical or PSC lens opacities, too advanced to grade (hyper
mature cataract); and history of previous surgery.
2) OR = Odds Ratio, CI = Confidence Interval ，* stands for p-value < 0.05, ** stands for p-value <
0.01.
3) Odds ratios represented associations per 1 standard deviation change in TRAP. See Table 2 for the
SDs for each TRAP metric.
4) Univariate associations were analyzed by logistic regression and the statistical significance were
presented with 95% CI and p-values.

16

Multivariate Associations of TRAP and lens opacities
The multivariate associations between TRAP and each category of lens opacities
are presented in Table 5.1 to Table 5.4. Again, we did not observe any statistically
significant associations between distance to nearest highway/freeway and any of the
studied outcomes. For all lens changes (Table 5.1), any lens opacity (Table 5.2) and
cortical-only lens opacity (Table 5.3), total road length in different buffers around home
showed positive associations with the development of lens opacity prevalence after
adjusting for age, sociodemographic and clinical risk factors. But these associations were
not statistically significant. The previously observed significant univariate associations of
total road length around 200m buffer around homes for all lens changes, any lens opacity
and cortical-only lens opacity were no longer statistically significant when adjusted for age.
17

Table 5.1. Multivariate Associations of Traffic-Related Air Pollution and All Lens Changes
Traffic Related Exposures
Model 1 Model 2 Model 3 Model 4
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
Distance to nearest
highway/freeway
0.96 0.96 0.97 0.97
(0.89- 1.04) (0.89- 1.04) (0.90- 1.05) (0.90- 1.06)
Total road length in 50m
buffer around home
0.99 1.00 1.01 1.01
(0.91- 1.07) (0.92- 1.08) (0.93- 1.09) (0.93- 1.09)
Total road length in 100m
buffer around home
0.96 0.97 0.97 0.97
(0.89- 1.04) (0.89- 1.05) (0.89- 1.05) (0.89- 1.05)
Total road length in 200m
buffer around home
1.03 1.03 1.02 1.02
(0.95- 1.11) (0.95- 1.11) (0.94- 1.11) (0.94- 1.11)
Total road length in 300m
buffer around home
0.99 0.99 0.99 0.99
(0.92- 1.07) (0.92- 1.07) (0.91- 1.07) (0.91- 1.07)
1) Model 1: Base model (only adjusted for age)
Model 2: Base model + sociodemographic indicators (annual household income < $20,000)
Model 3: Base model + sociodemographic indicators (annual household income < $20,000) +
clinical indicators (BMI, myopia, history of diabetes, hypertension and large drusen)
Model 4: Base model + sociodemographic indicators (annual household income < $20,000) +
clinical indicators (BMI, myopia, history of diabetes, hypertension and large drusen) + smoking
status
2) OR = Odds Ratio, CI = Confidence Interval. Odds ratios reflects associations per 1 standard
deviation (presented in table 3) change in TRAP metrics.
3) Multivariate associations were analyzed by logistic regression and the statistical significance were
presented with ORs (95%CIs).

18

Table 5.2. Multivariate Associations of Traffic-Related Air Pollution and Any Type of Lens Opacity
Traffic Related Exposures
Model 1 Model 2 Model 3 Model 4
OR OR OR OR
(95% CI) (95% CI) (95% CI) (95% CI)
Distance to nearest
highway/freeway
0.95 0.96 0.96 0.96
(0.87- 1.03) (0.88- 1.04) (0.89- 1.05) (0.89- 1.05)
Total road length in 50m
buffer around home
0.98 1.00 1.01 1.01
(0.91- 1.07) (0.92- 1.08) (0.93- 1.10) (0.93- 1.10)
Total road length in 100m
buffer around home
0.97 0.98 0.98 0.98
(0.89- 1.05) (0.90- 1.06) (0.90- 1.06) (0.90- 1.06)
Total road length in 200m
buffer around home
1.04 1.04 1.03 1.03
(0.96- 1.13) (0.96- 1.13) (0.95- 1.12) (0.95- 1.12)
Total road length in 300m
buffer around home
1.00 0.99 0.99 0.99
(0.92- 1.08) (0.92- 1.08) (0.92- 1.08) (0.91- 1.08)
1) Model 1: Base model (only adjusted for age)
Model 2: Base model + sociodemographic indicators (annual household income < $20,000)
Model 3: Base model + sociodemographic indicators (annual household income < $20,000) + clinical
indicators (BMI, myopia, history of diabetes, hypertension and large drusen)
Model 4: Base model + sociodemographic indicators (annual household income < $20,000) + clinical
indicators (BMI, myopia, history of diabetes, hypertension and large drusen) + smoking status
2) OR = Odds Ratio, CI = Confidence Interval. Odds ratios reflects associations per 1 standard deviation
(presented in table 3) change in TRAP metrics.
3) Multivariate associations were analyzed by logistic regression and the statistical significance were
presented with ORs (95%CIs).

19

Table 5.3. Multivariate Associations of Traffic-Related Air Pollution and Cortical-Only Lens Opacity
Traffic Related Exposures
Model 1 Model 2 Model 3 Model 4
OR OR OR OR
(95% CI) (95% CI) (95% CI) (95% CI)
Distance to nearest
highway/freeway
0.96 0.97 0.98 0.98
(0.87- 1.06) (0.87- 1.07) (0.88- 1.08) (0.88- 1.09)
Total road length in 50m buffer
around home
0.91 0.92 0.93 0.93
(0.82- 1.01) (0.83- 1.02) (0.83- 1.03) (0.83- 1.03)
Total road length in 100m
buffer around home
0.94 0.95 0.95 0.94
(0.84- 1.04) (0.85- 1.05) (0.85- 1.05) (0.85- 1.05)
Total road length in 200m
buffer around home
1.06 1.06 1.05 1.05
(0.95- 1.17) (0.95- 1.17) (0.95- 1.16) (0.94- 1.16)
Total road length in 300m
buffer around home
0.99 0.99 0.99 0.99
(0.90- 1.10) (0.90- 1.10) (0.89- 1.09) (0.89- 1.09)
1) Model 1: Base model (only adjusted for age)
Model 2: Base model + sociodemographic indicators (annual household income < $20,000)
Model 3: Base model + sociodemographic indicators (annual household income < $20,000) + clinical
indicators (BMI, myopia, history of diabetes, hypertension and large drusen)
Model 4: Base model + sociodemographic indicators (annual household income < $20,000) + clinical
indicators (BMI, myopia, history of diabetes, hypertension and large drusen) + smoking status
2) OR = Odds Ratio, CI = Confidence Interval. Odds ratios reflects associations per 1 standard deviation
(presented in table 3) change in TRAP metrics.
3) Multivariate associations were analyzed by logistic regression and the statistical significance were
presented with ORs (95%CIs).

We found statistically significant positive associations between total road lengths
in 200m buffer around home and nuclear-only lens opacity (Table 5.4). After adjusting for
age, sociodemographic and clinical risk factors and smoking status, 1SD higher total road
lengths around home within 200m buffer was associated with 1.20 times [95% CI = (1.01-
1.41)] prevalence of nuclear-only type of lens opacity. While 1SD higher total road lengths
20

within 50m and 100m buffers were associated with 15% higher odds of nuclear-only
cataract, the associations were marginally significant (both p < 0.10).

Table 5.4. Multivariate Associations of Traffic-Related Air Pollution and Nuclear-Only Lens Opacity
Traffic Related Exposures
Model 1 Model 2 Model 3 Model 4
OR OR OR OR
(95% CI) (95% CI) (95% CI) (95% CI)
Distance to nearest
highway/freeway
0.90 0.91 0.92 0.92
(0.76- 1.05) (0.77- 1.07) (0.78- 1.09) (0.78- 1.09)
Total road length in 50m
buffer around home
1.12 1.13 1.16 1.15
(0.96- 1.31) (0.97- 1.32) (0.99- 1.36) (0.98- 1.35)
Total road length in 100m
buffer around home
1.12 1.13 1.15 1.15
(0.96- 1.31) (0.97- 1.33) (0.98- 1.35) (0.98- 1.35)
Total road length in 200m
buffer around home
1.25 1.24 1.20 1.20
(1.06- 1.47)* (1.05- 1.46)* (1.02- 1.42)* (1.01- 1.41)*
Total road length in 300m
buffer around home
1.10 1.09 1.10 1.10
(0.94- 1.29) (0.93- 1.28) (0.93- 1.30) (0.93- 1.29)
1) Model 1: Base model (only adjusted for age)
Model 2: Base model + sociodemographic indicators (annual household income < $20,000)
Model 3: Base model + sociodemographic indicators (annual household income < $20,000) + clinical
indicators (BMI, myopia, history of diabetes, hypertension and large drusen)
Model 4: Base model + sociodemographic indicators (annual household income < $20,000) + clinical
indicators (BMI, myopia, history of diabetes, hypertension and large drusen) + smoking status
2)OR = Odds Ratio, CI = Confidence Interval. Odds ratios reflects associations per 1 standard deviation
(presented in table 3) change in TRAP metrics.
3) Multivariate associations were analyzed by logistic regression and the statistical significance were
presented with ORs (95%CIs). * stands for p-value < 0.05.

21

Discussion
In a large, population-based study among Latinos living in Los Angles, we found
that exposures to TRAP (as measured by road lengths around homes) were associated with
higher odds of nuclear-only lens opacity after accounting for potential confounders. While
living close to highways/freeways were associated with the higher prevalence of all types
of lens opacity, these findings did not reach statistical significance. Furthermore, we
observed associations between total road lengths within 200m buffer with higher odds of
all types of lens changes and opacities; however, these associations were only statistically
significant for nuclear-only lens opacity in the final models with 20% higher odds.
Our findings add to a growing body of evidence that air pollutants from tobacco
smoke,
26,30,31
, indoor cooking,
22,23
and TRAP exposures are associated with increased risk
of lens opacity or cataract development. Oxidative stress has been implicated as one of the
major putative mechanisms for these associations.
32
Oxidative stress involves free
superoxide and reactive oxidative intermediates like reactive oxygen species (ROS), which
could damage biological macromolecules and lead to the destruction of their structure and
functions in cell.
33
The accumulation of lipid and insoluble protein aggregations caused by
oxidative destruction in crystalline lens are important changes in development of lens
opacity.
34
Experimental results in diabetic rats demonstrated that oxidative stress results in
mitochondrial dysfunction and lead to cellular ATP depletion and reduce activity of
ATPases such as Ca
2+
ATPase.
35
Ca
2+
ATPase pumps Ca
2+
out of the cell to maintain low
22

concentration of intracellular Ca
2+
. Compared to clear lens, total calcium in cataract lens is
about 2.3 times higher for overall cataracts, and this ratio is especially higher for nuclear
cataract (2-26 times).
36

In addition, reduced glutathione (GSH) is the major source of intracellular
antioxidants, and the ratio of GSH/oxidized glutathione (GSSG) is a key indicator of the
oxidative damage to lens.
30
GSH could inhibit oxidants from damaging the functions of
cytoskeletal proteins, membrane proteins and enzymes like Na/K-ATPase.
31
Oxidative
damage from TRAP and other air pollutants may reduce GSH level and thereby promote
development of lens opacity. In a study conducted among children (8–13 years old) in India
who were exposed to biomass fuel smoke, serum GSH/GSSG ratio and concentration of
other antioxidants were found to be significantly lower than those who were exposed to
cleaner fuel.
37
In vitro and in vivo experiments on rat lenses and pigmented rats showed
that both cigarette and firewood smoke condensate could induce lens opacity and this
damage could be partially inhibited by antioxidants.
17
Current literature documented that
antioxidant pattern diet was inversely associated with the risk of cataract,
38,39
which adds
further evidence that oxidative stress may explain the association between lens opacity and
TRAP.
One explanation of finding robust associations between environmental pollutants
such as TRAP, tobacco smoke and indoor cooking and nuclear cataract only is that GSH
level is significantly higher in lens cortex and 80% to 90% lower in the nucleus of the
23

lens,
40
making the nucleus more vulnerable to oxidative damage from air pollution. In
rabbit lenses, treatment with hyperbaric oxygen has been shown to reduce GSH levels by
10% in lens cortex whereas it resulted in 70% reduction in nucleus.
41
Our findings of
statistically significant association between TRAP and development of nuclear-only lens
opacity but not with other types of lens opacity is consistent with some of the earlier
findings of associations between tobacco smoke exposure and indoor coking. Ye et al, in
their meta-analysis, found that current smoking was associated with both nuclear-only and
PSC cataract.
13
However, we did not have adequate sample size to investigate association
between TRAP and PSC type. Ravilla et al found that women who used kerosene for
cooking had statistically significant increased risk of nuclear only and PSC type but not
with cortical cataract.
23
Again, due to sample size we were unable to examine the role of
TRAP on PSC type.
While we found significant associations with the length of road around homes, we
did not find significant associations with the distance to highway/freeway in our study. One
explanation could be that the length of road metric is a better surrogate for TRAP exposures
from all roads around home and not limited to only one source of exposure such as
highway/freeway,
42,43
particularly in a traffic-congested city as Los Angeles. It is also
plausible that total road lengths around home with narrower buffers captures exposure from
specific noxious agents whereas others traffic exposure metrics may not. For example,
near-home exposures could be impacted by exposures related to acceleration and brake
24

wear due to traffic stop signs in residential areas.
43
We could also draw parallel from studies
of TRAP and exhaled nitric oxide levels (FeNO, a biomarker for airway inflammation), as
both eyes and lungs are exposed to the surrounding air. In studies investigating the role of
TRAP and FeNO, higher total road lengths were significantly associated with higher Fe NO,
but other TRAP metrics including sophisticated land-use regression and line dispersion-
based modeled residential-level TRAP exposures did not show statistically significant
associations.
25,42
Traffic count data in smaller roads around homes are often unavailable
from the state or city departments of transportation, which could limit estimation of near-
home TRAP exposure from existing exposure modeling approaches. Further research is
warranted to elucidate which specific component of near-home TRAP (e.g., fresh
gasoline/diesel exhaust, brake wear metals, re-suspended road dust) may impact
development of lens opacity.
There are several strengths of this study. The LALES is a well-characterized cohort
of Latino populations and the study have provided detailed data on sociodemographic,
clinical, and especially ophthalmologic characteristics. The lens-opacities were evaluated
and classified by trained clinicians following standard methods and protocols. We were
able to investigate the relationship of TRAP on overall and by different subtypes of lens
opacities. We also adjusted for potential confounders in our models. To the best of our
knowledge, this is the first study to report associations between TRAP and lens opacity. It
also adds to evidences that traffic pollution may be related with eye diseases in Latinos,
25

which emphasizes the importance of air quality control in residential areas of this ethnic
group.
Interpretation of our results requires consideration of some study limitations. This
was a cross-sectional study and we lacked information of duration of residency of the study
participants. While we adjusted sequentially several key class of variables in our models,
we did not find a major confounding effect of any variable in our models. However, we
acknowledge the possibility of residual confounding by these factors and possibility of
unmeasured confounding. We had 27 participants for PSC type of lens opacity in this
analysis and could not perform sub-group analysis to evaluate the relationship between
TRAP and PSC type due to the relatively small sample size. Given this study was
conducted among Latinos, the observed findings may not be generalizable to other
racial/ethnic groups.
Conclusion
In conclusion, in a cohort of Latinos aged over 40 years living in Los Angeles
County, we found that TRAP is associated with nuclear-only lens opacity. These results
are suggestive of possible associations of between TRAP and eye disease (lens opacity)
and warrants further investigation. Future longitudinal studies are urgently needed to obtain
refined estimates of exposures of near-home TRAP, with examination of their sources and
contributions and assess their impact on lens opacity. Our findings may have public health
26

implications regarding transportation planning in residential areas to reduce traffic
congestion and TRAP to protect adverse ocular health effects.
27

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Abstract (if available)

Abstract In epidemiologic studies, exposures to tobacco smoke and indoor cooking have been associated with lens opacity and oxidative stress has been implicated in these associations. Similarly, in urban population, traffic-related air pollution (TRAP) has been associated with such oxidative damages to multiple tissues. We hypothesized that exposure to TRAP is associated with development of lens opacities. We tested this hypothesis by using data from the Los Angeles Latino Eye Study (LALES), a population-based study among Latinos who were 40 years or older at study entry. Geographical Information System was used to estimate TRAP by using two surrogate measures of exposures: distance to the nearest highways/freeways from participants' home and total road lengths within 50m, 100m, 200m, and 300m circular buffers around home. We used multiple logistic regressions to investigate the associations of these TRAP metrics on prevalence of different types of lens opacities (all lens changes, any lens opacity, cortical-only lens opacity and nuclear-only lens opacity) in the LALES in 6,141 participants. In final multivariate model, we adjusted for age, income, body mass index, myopia, history of diabetes, systolic blood pressure and smoking status. In multivariate analysis, we found statistically significant associations with total road lengths around home and nuclear-only type of lens opacity but not with other types (any lens opacity and cortical-only). One standard deviation (SD) (372.11m) higher in total road length within 200m buffer around home was associated with 20% increase in prevalence of nuclear-only lens opacity [95% Confidence interval (95% CI) in odds ratio (OR) = (1.01-1.41)]. Similar associations were observed with road lengths within 50m and 100m buffers as well

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Asset Metadata

Creator Chen, Yi (author)

Core Title Association of traffic-related air pollution and lens opacities in the Los Angeles Latino Eye Study

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics and Epidemiology

Publication Date 02/06/2018

Defense Date 02/02/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Air pollution,cataract,Latino,lens opacity,OAI-PMH Harvest,oxidative stress,Traffic

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Choudhury, Farzana (committee chair), Lane, Christianne (committee member), Salam, Towhid (committee member)

Creator Email chenyi527@gmail.com,chenyi527@hotmail.com

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