Abstract of proposed work
The toxicological body of literature has presented a robust evidence for the role of
outdoor air pollution as a risk factor for type two diabetes (T2D). Similarly, the vast majority of
epidemiological studies conducted to date suggest that exposure to particulate matter and
nitrogen dioxide plays a role as a risk factor for T2D. However, a major literature gap currently
exists in the epidemiological body of evidence due to the limited number of studies published
and conflicting results reported by some studies.
The main objective of this proposed project is to fully investigate the relationship
between air pollution exposure and multiple T2D outcomes. Such objective is going to be
attained by constructing three major studies that attempt to better understand the relationship
between long- and short- term exposures to air pollutants and risk of T2D.
The short-term exposure element of this project is going to include two major studies
focused on the State of New Jersey. The first acute exposure study will investigate the
relationship between hourly ozone concentrations and apparent temperature, and morbidity
among those with diabetes. The second short-term exposure study will aim at estimating the
association between PM2.5, PM10 and NO2 exposures and mortality among those with diabetes.
The long-term exposure study will look into effects of PM2.5, PM10 and NO2 exposures,
and T2D incidence and prevalence. This study will utilize NHANES data to perform both a
cross-sectional analysis for each age group for multiple years, and a retrospective cohort study
that will follow different age groups back in time. Both short- and long- term studies will attempt
to better understand the impacts of traffic-related emissions and gender on T2D related
outcomes.
Exposures of interests in this study design are hourly ambient levels of PM2.5, PM10, NO2,
ozone and weather conditions in all residential and work areas where participants live. Direct
measurements form EPA air-quality monitors that correspond to participants’ residential
addresses are going to be used are primary indicators for PM2.5, PM10 and NO2 daily
concentrations. In case where air monitors are located outside a resident’s Zip code, data fusing
exposure estimation scenarios are going to be employed. The data fusing approach is going to
make use of an Inverse Distance Weighting Model in order to link Community Multi-Scale Air
Quality Model (CMAQ) with direct observation data. All hourly levels are going to be used to
estimate a daily moving mean as surrogate for daily levels of exposure to PM2.5, PM10 and NO2.
In case where air monitors are located outside a resident’s corresponding Zip codes, data
fusing exposure estimation scenarios are going to be employed. The data fusing approach is
going to make use of an Inverse Distance Weighting Model in order to link Community Multi-
Scale Air Quality Model (CMAQ) with direct observation data. In the case of the two acute
studies a case-cross-over study design is going to be implemented, while a Cox proportional
hazards model is going to be used in the chronic exposure study.
The toxicological body of literature has presented a robust evidence for the role of
outdoor air pollution as a risk factor for type two diabetes (T2D). Similarly, the vast majority of
epidemiological studies conducted to date suggest that exposure to particulate matter and
nitrogen dioxide plays a role as a risk factor for T2D. However, a major literature gap currently
exists in the epidemiological body of evidence due to the limited number of studies published
and conflicting results reported by some studies.
The main objective of this proposed project is to fully investigate the relationship
between air pollution exposure and multiple T2D outcomes. Such objective is going to be
attained by constructing three major studies that attempt to better understand the relationship
between long- and short- term exposures to air pollutants and risk of T2D.
The short-term exposure element of this project is going to include two major studies
focused on the State of New Jersey. The first acute exposure study will investigate the
relationship between hourly ozone concentrations and apparent temperature, and morbidity
among those with diabetes. The second short-term exposure study will aim at estimating the
association between PM2.5, PM10 and NO2 exposures and mortality among those with diabetes.
The long-term exposure study will look into effects of PM2.5, PM10 and NO2 exposures,
and T2D incidence and prevalence. This study will utilize NHANES data to perform both a
cross-sectional analysis for each age group for multiple years, and a retrospective cohort study
that will follow different age groups back in time. Both short- and long- term studies will attempt
to better understand the impacts of traffic-related emissions and gender on T2D related
outcomes.
Exposures of interests in this study design are hourly ambient levels of PM2.5, PM10, NO2,
ozone and weather conditions in all residential and work areas where participants live. Direct
measurements form EPA air-quality monitors that correspond to participants’ residential
addresses are going to be used are primary indicators for PM2.5, PM10 and NO2 daily
concentrations. In case where air monitors are located outside a resident’s Zip code, data fusing
exposure estimation scenarios are going to be employed. The data fusing approach is going to
make use of an Inverse Distance Weighting Model in order to link Community Multi-Scale Air
Quality Model (CMAQ) with direct observation data. All hourly levels are going to be used to
estimate a daily moving mean as surrogate for daily levels of exposure to PM2.5, PM10 and NO2.
In case where air monitors are located outside a resident’s corresponding Zip codes, data
fusing exposure estimation scenarios are going to be employed. The data fusing approach is
going to make use of an Inverse Distance Weighting Model in order to link Community Multi-
Scale Air Quality Model (CMAQ) with direct observation data. In the case of the two acute
studies a case-cross-over study design is going to be implemented, while a Cox proportional
hazards model is going to be used in the chronic exposure study.