Traffic-related air pollution and incident type 2 diabetes: results from the SALIA cohort study

The relationship between air pollution and diabetes: A study on the municipalities of the Metropolitan City of Milan

AIMS

Urbanisation has been linked with an increased risk of developing diabetes mellitus, dramatically worsening the healthcare system's financial burden. Environmental influences are emerging among the causing factors of the urban diabetes epidemic. We evaluated the relationship between air pollution and the prevalence of diabetes in the Municipalities of the Metropolitan City of Milan, comprising more than 3,4 million citizens.

METHODS

The prevalence of diabetes in the resident population and the mean annual air concentrations of PM10 and NO2 were retrieved from the municipal Agency for Health Protection and the regional Agency for Ambient Protection datasets. Two linear regression models were estimated to inspect the relationships between the (logit-based transformed) diabetes prevalence and air pollution concentrations, namely: (i) PM10, and (ii) NO2. Both models were adjusted for five control variables, including the qualitative variable year (2011-2018).

RESULTS

Both models highlight a statistically significant positive relationship between air pollutants and diabetes prevalence. An increase of one PM10 or NO2 concentrations' unit translates into a rise of 0.81% or 0.41% in diabetes prevalence, respectively.

CONCLUSION

Our results contribute to the ongoing research regarding health outcomes of urbanisation dynamics and should be considered in city planning policies.

Maternal exposure to air pollution and umbilical asprosin concentration, a novel insulin-resistant marker

Air pollution exposure during pregnancy has been associated with abnormal glucose hemostasis in the fetus, which may result in the programming of type 2 diabetes mellitus (T2DM) development in future life. Therefore, we investigated the association of maternal exposure to particulate matters (PMs) and traffic indicators with umbilical asprosin concentration, a novel insulin-resistant inducing adipokine, in newborns. Accordingly, 759 mother-newborn pairs from Sabzevar, Iran (2018-2019) participated in our study. Maternal exposure to PM₁, PM_2.5 and PM₁₀ concentrations was estimated using spatial-temporal models developed for the study area. The associations of exposure to traffic indicators (total street length in 100, 300 and 500 m buffers around home and proximity of mothers to nearest major roads) and air pollution with umbilical asprosin concentration were estimated using linear regression models, adjusted for potential confounders. The median (interquartile range (IQR)) of umbilical asprosin concentration was 30.4 (19.1) ng/mL. In fully adjusted models, each one IQR increase in PM₁₀ and PM_2.5 were associated with 26.43 ng/mL (95% CI: 10.97, 41.88) and 31.76 ng/mL (95% CI: 15.66, 47.86) increase in umbilical asprosin concentration, respectively. A similarity result was observed for total street length in 100 m buffer. An increase in proximity to major roads was associated with a decrease of -21.48 ng/mL (95% CI: 33.29, -9.67) in umbilical asprosin concentration. Our results suggested that maternal exposure to air pollution during pregnancy could increase the umbilical asprosin concentration. These novel findings may improve our understanding of the mechanisms whereby air pollutants impaired glucose hemostasis during the fetal period.

Associations of long-term exposure to ambient nitrogen dioxide with indicators of diabetes and dyslipidemia in China: A nationwide analysis

BACKGROUND

The associations between ambient NO2 and diabetes and dyslipidemia have been controversial, and data is especially lacking in developing countries.

OBJECTIVE

This study aimed to assess the associations of long-term exposure to NO2 with diabetes and dyslipidemia in China.

METHODS

We conducted a cross-sectional study including 13,013 participants from the China Health and Retirement Longitudinal Study (CHRLS). The annual average concentrations of NO2 were estimated based on the residential addresses of participants. We applied logistic regression models to evaluate the associations of NO2 with diabetes and dyslipidemia, and linear regression models to assess the associations with blood biomarkers.

RESULTS

A total of 1933 diabetes cases (14.85%) and 1935 (14.87%) dyslipidemia cases were identified. Significant associations were observed between NO2 and risk of diabetes and dyslipidemia independent of PM2.5 and O3. For an interquartile range (IQR) increase in NO2 (12.39 μg/m3), we observed a 13% [odds ratio (OR): 1.13; 95% confidence interval (CI): 1.01, 1.26] increased risk of diabetes, 1.48% (95%CI: 0.51%, 2.46%) increase in glucose, 0.74% (95%CI: 0.19%, 1.29%) increase in glycosylated hemoglobin (HbA1c), 17% (OR: 1.17; 95% CI: 1.05, 1.31) increased risk of dyslipidemia, 4.62% (95%CI: 2.49%, 6.79%) increase in triglyceride, and a decrease of 2.96% (95%CI: 2.13%, 3.79%) in high-density lipoprotein. The associations of NO2 with glucose disorders were stronger among smokers.

CONCLUSIONS

Our study indicated long-term exposure to NO2 might contribute to the development of diabetes and dyslipidemia, and the associations were potentially independent of O3 and PM2.5.

Endothelial progenitor cells as critical mediators of environmental air pollution-induced cardiovascular toxicity

Environmental air pollution exposure is a leading cause of death worldwide, and with increasing industrialization and urbanization, its disease burden is expected to rise even further. The majority of air pollution exposure-associated deaths are linked to cardiovascular disease (CVD). Although ample research demonstrates a strong correlation between air pollution exposure and CVD risk, the mechanisms by which inhalation of polluted air affects cardiovascular health are not completely understood. Inhalation of environmental air pollution has been associated with endothelial dysfunction, which suggests that air pollution exposure impacts CVD health by inducing endothelial injury. Interestingly, recent studies demonstrate that air pollution exposure affects the number and function of endothelial progenitor cells (EPCs), subpopulations of bone marrow-derived proangiogenic cells that have been shown to play an essential role in maintaining cardiovascular health. In line with their beneficial function, chronically low levels of circulating EPCs and EPC dysfunction (e.g., in diabetic patients) have been associated with vascular dysfunction, poor cardiovascular health, and increases in the severity of cardiovascular outcomes. In contrast, treatments that improve EPC number and function (e.g., exercise) have been found to attenuate cardiovascular dysfunction. Considering the critical, nonredundant role of EPCs in maintaining vascular health, air pollution exposure-induced impairments in EPC number and function could lead to endothelial dysfunction, consequently increasing the risk for CVD. This review article covers novel aspects and new mechanistic insights of the adverse effects of air pollution exposure on cardiovascular health associated with changes in EPC number and function.

Long-Term Air Pollution and Blood Pressure in an African American Cohort: the Jackson Heart Study

INTRODUCTION

African Americans are disproportionately affected by high blood pressure, which may be associated with exposure to air pollutants, such as fine particulate matter and ozone.

METHODS

Among African American Jackson Heart Study participants, this study examined associations between 1-year and 3-year mean fine particulate matter and ozone concentrations with prevalent and incident hypertension at Visits 1 (2000-2004, n=5,191) and 2 (2005-2008, n=4,105) using log binomial regression. Investigators examined associations with systolic blood pressure, diastolic blood pressure, pulse pressure, and mean arterial pressure using linear regression and hierarchical linear models, adjusting for sociodemographic, behavioral, and clinical characteristics. Analyses were conducted in 2017-2019.

RESULTS

No associations were observed between fine particulate matter or ozone concentration and prevalent or incident hypertension. In linear models, an IQR increase in 1-year ozone concentration was associated with 0.67 mmHg higher systolic blood pressure (95% CI=0.27, 1.06), 0.42 mmHg higher diastolic blood pressure (95% CI=0.20, 0.63), and 0.50 mmHg higher mean arterial pressure (95% CI=0.26, 0.74). In hierarchical models, fine particulate matter was inversely associated with systolic blood pressure (-0.72, 95% CI= -1.31, -0.13), diastolic blood pressure (-0.69, 95% CI= -1.02, -0.36), and mean arterial pressure (-0.71, 95% CI= -1.08, -0.33). Attenuated associations were observed with 1-year concentrations and at Visit 1.

CONCLUSIONS

Positive associations were observed between ozone and systolic blood pressure, diastolic blood pressure, and mean arterial pressure, and inverse associations between fine particulate matter and systolic blood pressure, diastolic blood pressure, and mean arterial pressure in an African American population with high (56%) prevalence of hypertension. Effect sizes were small and may not be clinically relevant.

Long-term exposure to ozone and sulfur dioxide increases the incidence of type 2 diabetes mellitus among aged 30 to 50 adult population

AIMS/HYPOTHESIS

Worldwide, the information regarding the associations between long-term exposure to ozone (O₃) and sulfur dioxide (SO₂) and the development of type 2 diabetes remains scarce, especially in Asia. This study aimed to investigate the long-term effects of exposure to ambient O₃ and SO₂ on the incidence of type 2 diabetes with consideration of other air pollutants in Taiwanese adults aged 30 to 50 years.

METHODS

A total of 6,426,802 non-diabetic participants aged between 30 and 50 years old were obtained from the National Health Insurance Research Database between 2005 and 2016. Incident type 2 diabetes was the main diagnosis at medical visits. Air quality data were provided by the Taiwan Environmental Protection Administration. The air pollutant concentrations for each participant were estimated using the ordinary kriging method to interpolate daily concentrations of O₃, SO₂, carbon monoxide (CO), nitrogen dioxide (NO₂), suspended fine particles (with an aerodynamic diameter less than 2.5 μm; PM_2.5), and suspended particles (with an aerodynamic diameter less than 10 μm; PM₁₀) in residential districts across Taiwan. Six-year average concentrations of pollutants were calculated from January 1, 2005 to December 31, 2010, and data were categorized into quartiles. We performed Cox regression models to analyze the long-term effects of exposure to O₃ and SO₂ on the incidence of type 2 diabetes.

RESULTS

The hazard ratio (HR) for the incidence of diabetes per each interquartile range (IQR) increase in ozone exposure (3.30 ppb) was 1.058 (95% confidence interval (CI): 1.053, 1.064) and 1.011 (95% CI: 1.007, 1.015) for SO₂ exposure (1.77 ppb) after adjusting for age, sex, socioeconomic status, urbanization level, temperature, humidity, and chronic comorbidities (Model 3). Furthermore, for every 3.30 ppb increase of O₃, the HR for incident type 2 diabetes was 1.093 (95% CI: 1.087, 1.100) after controlling factors shown in Model 3 plus SO₂ and PM_2.5. On the other hand, for every 1.77 ppb increase of SO₂, the HR for incident type 2 diabetes was 1.073 (95% CI: 1.068, 1.079) after controlling factors shown in Model 3 plus NO₂ and PM_2.5.

CONCLUSIONS

Long-term exposure to ambient O₃ and SO₂ was associated with a higher risk of developing type 2 diabetes for Taiwanese population. Exposure to O₃ and SO₂ may play a role in the adult early-onset type 2 diabetes.

Influencing Indicators and Spatial Variation of Diabetes Mellitus Prevalence in Shandong, China: A Framework for Using Data-Driven and Spatial Methods

To control and prevent the risk of diabetes, diabetes studies have identified the need to better understand and evaluate the associations between influencing indicators and the prevalence of diabetes. One constraint has been that influencing indicators have been selected mainly based on subjective judgment and tested using traditional statistical modeling methods. We proposed a framework new to diabetes studies using data-driven and spatial methods to identify the most significant influential determinants of diabetes automatically and estimated their relationships. We used data from diabetes mellitus patients' health insurance records in Shandong province, China, and collected influencing indicators of diabetes prevalence at the county level in the sociodemographic, economic, education, and geographical environment domains. We specified a framework to identify automatically the most influential determinants of diabetes, and then established the relationship between these selected influencing indicators and diabetes prevalence. Our autocorrelation results showed that the diabetes prevalence in 12 Shandong cities was significantly clustered (Moran's I = 0.328, p < 0.01). In total, 17 significant influencing indicators were selected by executing binary linear regressions and lasso regressions. The spatial error regressions in different subgroups were subject to different diabetes indicators. Some positive indicators existed significantly like per capita fruit production and other indicators correlated with diabetes prevalence negatively like the proportion of green space. Diabetes prevalence was mainly subjected to the joint effects of influencing indicators. This framework can help public health officials to inform the implementation of improved treatment and policies to attenuate diabetes diseases.

Longitudinal associations between ambient air pollution and insulin sensitivity: results from the KORA cohort study

BACKGROUND

Impaired insulin sensitivity could be an intermediate step that links exposure to air pollution to the development of type 2 diabetes. However, longitudinal associations of air pollution with insulin sensitivity remain unclear. Our study investigated the associations of long-term air pollution exposure with the degree and rate of change of insulin sensitivity.

METHODS

In this longitudinal study, we analysed data from the Cooperative Health Research in the Region of Augsburg (KORA) cohort from Augsburg, Germany, which recruited participants aged 25-74 years in the survey between 1999 and 2001 (KORA S4), with two follow-up examinations in 2006-08 (KORA F4) and 2013-14 (KORA FF4). Serum concentrations of fasting insulin and glucose, and homoeostasis model assessment of insulin resistance (HOMA-IR, a surrogate measure of insulin sensitivity) and β-cell function (HOMA-B, a surrogate marker for fasting insulin secretion) were assessed at up to three visits between 1999 and 2014. Annual average air pollutant concentrations at the residence were estimated by land-use regression models. We examined the associations of air pollution with repeatedly assessed biomarker levels using mixed-effects models, and we assessed the associations with the annual rate of change in biomarkers using quantile regression models.

FINDINGS

Among 9620 observations from 4261 participants in the KORA cohort, we included 6008 (62·5%) observations from 3297 (77·4%) participants in our analyses. Per IQR increment in annual average air pollutant concentrations, HOMA-IR significantly increased by 2·5% (95% CI 0·3 to 4·7) for coarse particulate matter, by 3·1% (0·9 to 5·3) for PM_2·5, by 3·6% (1·0 to 6·3) for PM_{2·5absorbance}, and by 3·2% (0·6 to 5·8) for nitrogen dioxide, and borderline significantly increased by 2·2% (-0·1 to 4·5) for ozone, whereas it did not significantly increase for the whole range of ultrafine particles. Similar positive associations in slightly smaller magnitude were observed for HOMA-B and fasting insulin levels. In addition, air pollutant concentrations were positively associated with the annual rate of change in HOMA-IR, HOMA-B, and fasting insulin. Neither the level nor the rate of change of fasting glucose were associated with air pollution exposure.

INTERPRETATION

Our study indicates that long-term air pollution exposure could contribute to the development of insulin resistance, which is one of the key factors in the pathogenesis of type 2 diabetes.

FUNDING

German Federal Ministry of Education and Research.

Association of Long-Term Air Pollution with Prevalence and Incidence of Distal Sensorimotor Polyneuropathy: KORA F4/FF4 Study

BACKGROUND

Air pollution contributes to type 2 diabetes and cardiovascular diseases, but its relevance for other complications of diabetes, in particular distal sensorimotor polyneuropathy (DSPN), is unclear. Recent studies have indicated that DSPN is also increasingly prevalent in obesity.

OBJECTIVES

We aimed to assess associations of air pollutants with prevalent and incident DSPN in a population-based study of older individuals with high rates of type 2 diabetes and obesity.

METHODS

Cross-sectional analyses on prevalent DSPN were based on 1,075 individuals 62-81 years of age from the German Cooperative Health Research in the Region of Augsburg (KORA) F4 survey (2006-2008). Analyses on incident DSPN included 424 individuals without DSPN at baseline (KORA F4), of whom 188 had developed DSPN by the KORA FF4 survey (2013-2014). Associations of annual average air pollutant concentrations at participants' residences with prevalent and incident DSPN were estimated using Poisson regression models with a robust error variance adjusting for multiple confounders.

RESULTS

Higher particle number concentrations (PNCs) were associated with higher prevalence [risk ratio (RR) per interquartile range (IQR) increase=1.10 (95% CI: 1.01, 1.20)] and incidence [1.11 (95% CI: 0.99, 1.24)] of DSPN. In subgroup analyses, particulate (PNC, PM10, PMcoarse, PM2.5, and PM2.5abs) and gaseous (NOx, NO2) pollutants were positively associated with prevalent DSPN in obese participants, whereas corresponding estimates for nonobese participants were close to the null [e.g., for an IQR increase in PNC, RR=1.17 (95% CI: 1.05, 1.31) vs. 1.06 (95% CI: 0.95, 1.19); pinteraction=0.22]. With the exception of PM2.5abs, corresponding associations with incident DSPN were positive in obese participants but null or inverse for nonobese participants, with pinteraction≤0.13 [e.g., for PNC, RR=1.28 (95% CI: 1.08, 1.51) vs. 1.03 (95% CI: 0.90, 1.18); pinteraction=0.03].

DISCUSSION

Both particulate and gaseous air pollutants were positively associated with prevalent and incident DSPN in obese individuals. Obesity and air pollution may have synergistic effects on the development of DSPN. https://doi.org/10.1289/EHP7311.

Continuous exposure to ambient air pollution and chronic diseases: prevalence, burden, and economic costs

Studies that assess the connection between the prevalence of chronic diseases and continuous exposure to air pollution are scarce in developing countries, mainly due to data limitations. Largely overcoming data limitations, this study aimed to investigate the association between the likelihood of reporting a set of chronic diseases (diabetes, cancer, stroke and myocardial infarction, asthma, and hypertension) and continuous exposure to carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), and coarse particulate matter (PM10). Using the estimated associations, the disease burden and economic costs of continuous exposure to air pollutants were also approximated. A 2011 Health Equity Assessment and Response Tool survey from Tehran, Iran, was used in the main analyses. A sample of 67,049 individuals who had not changed their place of residence for at least 2 years before the survey and reported all relevant socioeconomic information was selected. The individuals were assigned with the average monthly air pollutant levels of the nearest of 16 air quality monitors during the 2 years leading to the survey. Both single- and multi-pollutant analyses were conducted. The country's annual household surveys from 2002 to 2011 were used to calculate the associated economic losses. The single-pollutant analysis showed that a one-unit increase in monthly CO (ppm), NO₂ (ppb), O₃ (ppb), and PM10 (μg/m³) during the 2 years was associated with 751 [confidence interval (CI): 512-990], 18 (CI: 12-24), 46 (CI: -27-120), and 24 (CI: 13-35) more reported chronic diseases in 100,000, respectively. The disease-specific analyses showed that a unit change in average monthly CO was associated with 329, 321, 232, and 129 more reported cases of diabetes, hypertension, stroke and myocardial infarction, and asthma in 100,000, respectively. The measured associations were greater in samples with older individuals. Also, a unit change in average monthly O₃ was associated with 21 (in 100,000) more reported cases of asthma. The multi-pollutant analyses confirmed the results from single-pollutant analyses. The supplementary analyses showed that a one-unit decrease in monthly CO level could have been associated with about 208 (CI: 147-275) years of life gained or 15.195 (CI: 10.296-20.094) thousand US dollars (USD) in life-time labor market income gained per 100,000 30-plus-year-old Tehranis.

Effect of ambient air pollution on the incidence of colorectal cancer among a diabetic population: a nationwide nested case-control study in Taiwan

OBJECTIVES

An increasing number of studies had shown that air pollution exposure may aggravate blood glucose control in patients with diabetes, an independent risk factor for colorectal cancer (CRC) proposed by some researchers. This study aimed to investigate the impact of exposure to ambient particulate matter with aerodynamic diameters ≤2.5 μm (PM_2.5) on the incidence of CRC among a diabetic population.

DESIGN

A nested case-control study.

SETTING

A subset data retrieved from the Taiwan's National Health Insurance Research Database.

PARTICIPANTS

We identified patients with newly diagnosed diabetes (n=1 164 962) during 1999-2013. Participants who had subsequently developed an incident of CRC were placed into the case group, while controls were matched to the cases at a 4:1 ratio by age, gender, date of diabetes diagnosis and the index date of CRC diagnosis.

METHODS AND OUTCOME MEASURES

All variables associated with the risk of CRC entered into a multinomial logistic regression model. The dose-response relationship between various average concentrations of PM_2.5 exposure and the incidence of CRC was estimated by logistic regression.

RESULTS

The study included a total of 7719 incident CRC cases matched with 30 876 controls of random sampling. The mean annual concentration of PM_2.5 was 35.3 µg/m³. After adjusting for potential confounders, a dose-response relationship was observed between the CRC risks and each interquartile increase of PM_2.5 concentration (Q1-Q2: 1.03 (0.95-1.11), Q2-Q3: 1.06 (0.98-1.15), ≥Q3: 1.19 (1.10-1.28) in model 2. The adjusted ORs (95% CI) of CRC incidence for each 10 µg/m³ increment of PM_2.5 was 1.08 (1.04-1.11). Moreover, a faster growing adapted Diabetes Complications Severity Index (aDCSI) score was noticed in CRC group compared with the controls, which also showed a significant association in our multivariate analysis (adjusted OR=1.28, 95% CI 1.18 to 1.38).

CONCLUSIONS

Long-term exposure to high concentrations of PM_2.5 may contribute to an increased incidence of CRC among diabetic populations.

Characterization of particles emitted by pizzerias burning wood and briquettes: a case study at Sao Paulo, Brazil

The burning of biomass in pizza ovens can be an important source of air pollution. Fine particulate matter represents one of the most aggressive pollutants to human health, besides the potential to interfere with global radiative balance. A study in real-world condition was performed in three pizzerias in São Paulo city. Two of the pizzerias used eucalyptus timber logs and one used wooden briquettes. The results from the three pizzerias revealed high average concentrations of PM_2.5: 6171.2 μg/m³ at the exit of the chimney and 68.2 μg/m³ in indoor areas. The burning of briquette revealed lower concentrations of PM_2.5. BC represented approximately 20% and 30% of the PM_2.5 mass concentration in indoor and at chimney exhaust, respectively. Among the trace elements, potassium, chlorine and sulphur were the most prevalent in terms of concentration. Scanning electron microscopy (SEM) analysis revealed particles with an individual and spherical morphology, i.e. the conglomeration of spherical particles, flattened particles in the formation of fibres, the overlapping of layers and the clustering of particles with sponge-like qualities. The average emission factors for PM_2.5 and BC due to the burning of logs were 0.38 g/kg and 0.23 g/kg, respectively. The total emissions of PM_2.5 and BC were 116.73 t/year and 70.65 t/year, respectively, in the burning of timber logs.

Increase of Cardiometabolic Biomarkers Among Vehicle Inspectors Exposed to PM0.25 and Compositions

Background

Exposure to particulate matter (PM) emitted from vehicle exhaust might disrupt systemic function and elevate the risk of cardiovascular disease. In this study, we examined the changes of cardiometabolic biomarkers among vehicle inspectors exposed daily to PM0.25 and components.

Methods

This cross-sectional study was conducted at two vehicle inspection centers, Pulogadung and Ujung Menteng, located in East Jakarta, Indonesia. The exposed respondents were 43 workers from vehicle inspection centers, and the unexposed group consisted of 22 staff officers working in the same locations. Vehicle exhaust particulate matter was measured for eight hours using a Leland Legacy personal pump attached to a Sioutas Cascade Impactor. The used filters were 25 and 37-mm quartz filters. The particulate matter concentration was analyzed using a gravimetric method, whereas trace elements were analyzed using energy dispersive X-ray fluorescence. An EEL Smoke Stain Reflectometer analyzed black carbon.

Results

The personal exposure concentrations of PM0.25 were 10.4-fold higher than those in unexposed groups. Calcium and sulfur were the major components in the obtained dust, and their levels were 3.3- and 7.2-fold higher, respectively, in the exposed group. Based on an independent-samples t-test, high-density lipoprotein, triglyceride, HbA1c, total immunoglobulin E, high-sensitivity C-reactive protein, tumor necrosis factor-alpha, and nitric oxide levels were significantly different between the groups.

Conclusions

In summary, it was suggested that PM0.25 exposure from vehicle exhaust might affect cardiometabolic biomarkers change.

Exposure to urban particulate matter and its association with human health risks

Human health and environmental risks are increasing following air pollution associated with vehicular and industrial emissions in which particulate matter is a constituent. The purpose of this review was to assess studies on the health effects and mortality induced by particles published for the last 15 years. The literature survey indicated the existence of strong positive associations between fine and ultrafine particles' exposure and cardiovascular, hypertension, obesity and type 2 diabetes mellitus, cancer health risks, and mortality. Its exposure is also associated with increased odds of hypertensive and diabetes disorders of pregnancy and premature deaths. The ever increasing hospital admission and mortality due to heart failure, diabetes, hypertension, and cancer could be due to long-term exposure to particles in different countries. Therefore, its effect should be communicated for legal and scientific actions to minimize emissions mainly from traffic sources.

Associations between the incidence and mortality rates of type 2 diabetes mellitus and long-term exposure to ambient air pollution: A 12-year cohort study in northern China

BACKGROUND

Ambient air pollution has recently been related to type 2 diabetes mellitus (T2DM), a disease that has caused an economic and health burden worldwide. Evidence of an association between air pollution and T2DM was reported in the United States and Europe. However, few studies have focused on the association with high levels of air pollutants in a developing country.

OBJECTIVES

We conducted a 12-year cohort study to assess the incidence and mortality of T2DM associated with long-term exposure to PM₁₀, SO₂, and NO₂.

METHODS

A retrospective cohort with participants from four cities in northern China was conducted to assess mortality and incidence of T2DM from 1998 to 2009. Incidence of T2DM was self-reported, and incident intake of an antidiabetic drug or injection of insulin simultaneously and mortality of T2DM was obtained from a family member and double checked against death certificates provided from the local center for disease control and prevention. Individual pollution exposures were the mean concentrations of pollutants estimated from the local environmental monitoring centers over the survival years. Hazard ratios (HRs) were estimated using Cox regression models after adjusting for potential confounding factors.

RESULTS

A total of 39 054 participants were recruited into the mortality cohort, among which 59 subjects died from T2DM; 38 529 participants were analyzed in the incidence cohort, and 1213 developed new cases of T2DM. For each 10 μg/m³ increase in PM₁₀, SO₂, and NO₂, the adjusted HRs and 95% confidence interval (CI) for diabetic incidence were 1.831 (1.778, 1.886), 1.287 (1.256, 1.318), and 1.472 (1.419, 1.528), respectively. Similar results can be observed in the analysis of diabetic mortality with HRs (95% CI) up to 2.260 (1.732, 2.950), 1.130 (1.042, 1.225), and 1.525 (1.280, 1.816), respectively.

CONCLUSIONS

Our results suggested that long-term exposure to high levels of PM₁₀, SO₂, and NO₂ increase risk of incident and mortality of T2DM in China.

Impacts of ambient air pollution on glucose metabolism in Korean adults: a Korea National Health and Nutrition Examination Survey study

BACKGROUND

Exposure to air pollution was reported to affect glucose metabolism, increasing the risk of diabetes mellitus. We conducted an epidemiological study on glucose metabolism and air pollution by exploring the levels of fasting blood glucose (FBG) and hemoglobin A1c (HbA1c) with changes in ambient air quality, depending on the characteristics of the susceptible population.

METHODS

We carried out a cross-sectional analysis of a nationally representative sample of 10,014 adults (4267 in male and 5747 in female) from the Korea National Health and Nutrition Examination Survey in 2012 and 2013 along with data from the Korean Air Quality Forecasting System. The analysis was performed using a generalized linear model stratified by sex, age, and presence of diabetes. We assessed the changes in FBG and HbA1c associated with exposures to particulate matter (PM₁₀), fine particulate matter (PM_2.5), and nitrogen dioxide (NO₂) after controlling for confounders.

RESULTS

There were 1110 participants with diabetes (557 in male and 553 in female). Overall, the FBG level increased by 7.83 mg/dL (95% confidence interval [CI]: 2.80-12.87) per interquartile range (IQR) increment of NO₂, 5.32 mg/dL (95% CI: 1.22-9.41) per IQR increment of PM₁₀ at a moving average of 0-6 days, and 4.69 mg/dL (95% CI: 0.48-8.91) per IQR increment of PM_2.5 at a moving average of 0-5 days. HbA1c increased by 0.57% (95% CI: 0.04-1.09) per IQR increment of PM₁₀ at a moving average of 0-60 days and 0.34% (95% CI: 0.04-0.63) per IQR increment of PM_2.5 at a moving average of 0-75 days. The change in FBG and HbA1c increased more in the diabetic group, especially in males aged 65 years or more. There was a strong association between elevation in diabetes-related parameters and exposure to air pollution.

CONCLUSIONS

Our study provides scientific evidence supporting that short- and mid-term exposure to air pollution is associated with changes in biological markers related to diabetes. This finding suggests that the impact of air pollution should be reflected in chronic disease management when establishing local health care policies.

The Nexus between Workplace Exposure for Wood, Welding, Motor Mechanic, and Oil Refinery Workers and the Prevalence of Prediabetes and Type 2 Diabetes Mellitus

Workplace exposure in various occupational and industrial sectors is an emerging health concern worldwide. This study aimed to investigate the nexus between workplace exposure for wood, welding, motor mechanic, and oil refinery workers and the prevalence of prediabetes and type 2 diabetes mellitus. Initially, 2500 male volunteers who were wood, welding, motor mechanic, and oil refinery workers were interviewed. After an examination of their demographics and medical history, 1408 non-smoking wood (158), welding (560), motor mechanic (272), and oil refinery workers (217), along with 201 control subjects, were selected. The participants' mean age was 36.59 ± 0.29 years and the mean body mass index was 26.14 ± 0.11 kg/m². The selected industry workers had been exposed to their respective wood, welding, motor mechanic, and oil refinery workplaces for 8 h per day, six days per week. The American Diabetic Association (ADA)-based glycated hemoglobin (HbA1c) criterion was used to diagnose prediabetes and type 2 diabetes mellitus. Subjects with an HbA1c of less than 5.7% were regarded as non-diabetics, subjects with an HbA1c of 5.7%-6.4% were considered prediabetics, and subjects with an HbA1c of more than 6.4% were considered diabetics. In wood industry workers, the prevalence of prediabetes (PD) was 64 (40.50%) and in type 2 diabetes mellitus (T2DM), it was 21 (13.29%); in welding workers, the prevalence of prediabetes was 261 (46.60%), and for T2DM, it was 90 (16.07%); in motor mechanic workers, the prevalence of prediabetes was 110 (40.44%), and for T2DM, it was 126 (46.32%); and in oil refinery workers, the prevalence of prediabetes was 80 (36.86%), and for T2DM, it was 35 (16.12%). However; the combined prevalence of prediabetes and T2DM among wood, welding, motor mechanic, and oil refinery workers was 421 (34.79%) and 515 (42.66%), respectively. The prevalence of prediabetes and T2DM among workers increased with the duration of working exposure in the wood, welding, motor mechanic, and oil refinery industries. A one-year working exposure in these industries caused an increase of 0.03% in HbA1c. Workplace exposure in wood, welding, motor mechanic, and oil refinery industries increased the risk of prevalence of prediabetes and T2DM among the workers and affected the diabetes etiology.

Targeting Glycoproteins as a therapeutic strategy for diabetes mellitus and its complications

OBJECTIVES

Glycoproteins are organic compounds formed from proteins and carbohydrates, which are found in many parts of the living systems including the cell membranes. Furthermore, impaired metabolism of glycoprotein components plays the main role in the pathogenesis of diabetes mellitus. The aim of this study is to investigate the influence of glycoprotein levels in the treatment of diabetes mellitus.

METHODS

All relevant papers in the English language were compiled by searching electronic databases, including Scopus, PubMed and Cochrane library. The keywords of glycoprotein, diabetes mellitus, glycan, glycosylation, and inhibitor were searched until January 2019.

RESULTS

Glycoproteins are pivotal elements in the regulation of cell proliferation, growth, maturation and signaling pathways. Moreover, they are involved in drug binding, drug transportation, efflux of chemicals and stability of therapeutic proteins. These functions, structure, composition, linkages, biosynthesis, significance and biological effects are discussed as related to their use as a therapeutic strategy for the treatment of diabetes mellitus and its complications.

CONCLUSIONS

The findings revealed several chemical and natural compounds have significant beneficial effects on glycoprotein metabolism. The comprehension of glycoprotein structure and functions are very essential and inevitable to enhance the knowledge of glycoengineering for glycoprotein-based therapeutics as may be required for the treatment of diabetes mellitus and its associated complications. Graphical abstract.

Relationship betweentemporal distribution of air pollution exposure and glucose homeostasis during pregnancy

BACKGROUND

Mounting evidence has demonstrated that air pollution exposure is associated with the increased prevalence of gestational diabetes mellitus (GDM). However, the long-term exposure effect and the time window of the maximum effect of these air pollutants on GDM and glucose homeostasis during pregnancy are unclear.

METHODS

We conducted this study on 5427 nondiabetic pregnant women who were admitted from three hospitals in Hefei City, China, between 2015 and 2018. The data regarding the average exposure to particulate matter (PM), sulfur dioxide (SO₂), and ozone (O₃) were estimated in a fixed monitoring station in Hefei. We used logistic regression and multiple linear regression to assess the effects of air pollutants on GDM and glucose homeostasis.

RESULTS

Of the 5427 participants, 1119 (20.6%) had GDM. We found prepregnancy exposure to air pollutants was associated with the risk of GDM in the single pollutant model [odds and 95% confidence interval (CI) of GDM for an interquartile range (IQR) increase was 1.24 (1.06-1.45) for PM_2.5, 1.42 (1.26-1.59) for PM₁₀, 1.21 (1.10-1.33) for SO₂ and1.19 (1.08-1.31) for O₃]. The risk of GDM before pregnancy was higher with long-term exposure to high-concentration pollutants compared with the risk in pregnant women who were not exposed to high-concentration pollutants (χ² = 41.52, p for trend <0.0001); the ORs and 95% CI values for the exposure times of 1, 2, and 3 months were 1.28 (0.96-1.72), 1.52 (1.06-2.19), and 1.69 (1.11-2.57), respectively. The results showed a positive effect of exposure to higher-concentration air pollutants 1 year before pregnancy on glucose homeostasis during pregnancy. The time windows of the maximum effect of PM_2.5, PM₁₀, SO₂, and O₃ on GDM were different. The time windows of the maximum effect of PM_2.5, PM₁₀, and SO₂ were 6 months, 5 months, and 1 month before the last menstrual period (LMP) and 3 months after the LMP, respectively. The time windows of the maximum effect of air pollution on glucose homeostasis indicators from the 2-h 75-g oral glucose tolerance test were similar to the abovementioned results.

CONCLUSIONS

Prepregnancy long-term air pollution exposure was associated with a higher risk of developing GDM by affecting glucose metabolism. The time window of the maximum effect of PM on GDM and glucose metabolism indicators was observed earlier than that of SO₂ and O₃.

Spatiotemporal trends and influence factors of global diabetes prevalence in recent years

Diabetes is one of the most widespread global epidemics and has become the main component of the global disease burden. Based on data regarding the prevalence of diabetes in 203 countries and territories from 2013 to 2017, we employed the Bayesian space-time model to investigate the spatiotemporal trends in the global diabetes prevalence. The factors influencing the diabetes prevalence were assessed by the Bayesian LASSO regression model. We identified 77 (37.9%) hotspots with a higher diabetes prevalence than the global average, 10 (0.4%) warm spots with global average level and 116 (57.1%) cold spots with lower level than global average. Of the 203 countries and territories, 68 (33.5%), including 31 hotspots, 5 warm spots and 32 cold spots, exhibited an increasing trend. Of these, 60 experienced an annual increase of more than 0.25%, and 8 showed an increasing trend. Three populous countries, namely China, the USA and Mexico, exhibited a high prevalence and an increasing trend simultaneously. Three socioeconomic factors, body mass index (BMI), urbanization rate (UR) and gross domestic product per capita (GDP-PC), and PM_2.5 pollution were found to significantly influence the prevalence of diabetes. BMI was the strongest factor; for every 1% increase in BMI, the prevalence of diabetes increased by 2.371% (95% confidence interval (95% CI): 0.957%, 3.890%) in 2013 and by 3.045% (95% CI: 1.803%, 4.397%) in 2015 and 2017. PM_2.5 pollution could be a risk factor, and its influencing magnitude gradually increased as well. With an annual PM_2.5 concentrations increase of 1.0% in a country, the prevalence of diabetes increased by 0.196% (95% CI: 0.020%, 0.356%). The UR, on the other hand, was found to be inversely associated with the prevalence of diabetes; with each UR increase of 1%, the prevalence of diabetes decreased by 0.006% (95% CI: 0.001%, 0.011%).

Association between long-term exposure to ambient air pollution and prevalence of diabetes mellitus among Malaysian adults

Background

Malaysia has the highest rate of diabetes mellitus (DM) in the Southeast Asian region, and has ongoing air pollution and periodic haze exposure.

Methods

Diabetes data were derived from the Malaysian National Health and Morbidity Surveys conducted in 2006, 2011 and 2015. The air pollution data (NO_x, NO₂, SO₂, O₃ and PM₁₀) were obtained from the Department of Environment Malaysia. Using multiple logistic and linear regression models, the association between long-term exposure to these pollutants and prevalence of diabetes among Malaysian adults was evaluated.

Results

The PM₁₀ concentration decreased from 2006 to 2014, followed by an increase in 2015. Levels of NO_x decreased while O₃ increased annually. The air pollutant levels based on individual modelled air pollution exposure as measured by the nearest monitoring station were higher than the annual averages of the five pollutants present in the ambient air. The prevalence of overall diabetes increased from 11.4% in 2006 to 21.2% in 2015. The prevalence of known diabetes, underdiagnosed diabetes, overweight and obesity also increased over these years. There were significant positive effect estimates of known diabetes at 1.125 (95% CI, 1.042, 1.213) for PM₁₀, 1.553 (95% CI, 1.328, 1.816) for O₃, 1.271 (95% CI, 1.088, 1.486) for SO₂, 1.124 (95% CI, 1.048, 1.207) for NO₂, and 1.087 (95% CI, 1.024, 1.153) for NO_x for NHMS 2006. The adjusted annual average levels of PM₁₀ [1.187 (95% CI, 1.088, 1.294)], O₃ [1.701 (95% CI, 1.387, 2.086)], NO₂ [1.120 (95% CI, 1.026, 1.222)] and NO_x [1.110 (95% CI, 1.028, 1.199)] increased significantly from NHMS 2006 to NHMS 2011 for overall diabetes. This was followed by a significant decreasing trend from NHMS 2011 to 2015 [0.911 for NO₂, and 0.910 for NO_x].

Conclusion

The findings of this study suggest that long-term exposure to O₃ is an important associated factor of underdiagnosed DM risk in Malaysia. PM₁₀, NO₂ and NO_x may have mixed effect estimates towards the risk of DM, and their roles should be further investigated with other interaction models. Policy and intervention measures should be taken to reduce air pollution in Malaysia.

Associations Between Long-Term Fine Particulate Matter Exposure and Mortality in Heart Failure Patients

Background Environmental health risks for individuals with heart failure (HF) have been inadequately studied, as these individuals are not well represented in traditional cohort studies. To address this we studied associations between long-term air pollution exposure and mortality in HF patients. Methods and Results The study population was a hospital-based cohort of individuals diagnosed with HF between July 1, 2004 and December 31, 2016 compiled using electronic health records. Individuals were followed from 1 year after initial diagnosis until death or the end of the observation period (December 31, 2016). We used Cox proportional hazards models to evaluate the association of annual average fine particulate matter (PM_2.5) exposure at the time of initial HF diagnosis with all-cause mortality, adjusted for age, race, sex, distance to the nearest air pollution monitor, and socioeconomic status indicators. Among 23 302 HF patients, a 1 μg/m³ increase in annual average PM_2.5 was associated with an elevated risk of all-cause mortality (hazard ratio 1.13; 95% CI, 1.10-1.15). As compared with people with exposures below the current national PM_2.5 exposure standard (12 μg/m³), those with elevated exposures experienced 0.84 (95% CI, 0.73-0.95) years of life lost over a 5-year period, an observation that persisted even for those residing in areas with PM_2.5 concentrations below current standards. Conclusions Residential exposure to elevated concentrations of PM_2.5 is a significant mortality risk factor for HF patients. Elevated PM_2.5 exposures result in substantial years of life lost even at concentrations below current national standards.

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data

BACKGROUND

Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any biomedical field that is challenged by the need for integrated spatiotemporal data to examine individual-level determinants of health and disease.

RESULTS

We have developed an open-source software application-FHIR PIT (Health Level 7 Fast Healthcare Interoperability Resources Patient data Integration Tool)-to enable studies on the impact of individual-level environmental exposures on health and disease. FHIR PIT was motivated by the need to integrate patient data derived from our institution's clinical warehouse with a variety of public data sources on environmental exposures and then openly expose the data via ICEES (Integrated Clinical and Environmental Exposures Service). FHIR PIT consists of transformation steps or building blocks that can be chained together to form a transformation and integration workflow. Several transformation steps are generic and thus can be reused. As such, new types of data can be incorporated into the modular FHIR PIT pipeline by simply reusing generic steps or adding new ones. We validated FHIR PIT in the context of a driving use case designed to investigate the impact of airborne pollutant exposures on asthma. Specifically, we replicated published findings demonstrating racial disparities in the impact of airborne pollutants on asthma exacerbations.

CONCLUSIONS

While FHIR PIT was developed to support our driving use case on asthma, the software can be used to integrate any type and number of spatiotemporal data sources at a level of granularity that enables individual-level study. We expect FHIR PIT to facilitate research in environmental health and numerous other biomedical disciplines.

All-source and source-specific air pollution and 10-year diabetes Incidence: Total effect and mediation analyses in the Heinz Nixdorf recall study

BACKGROUND

An increasing number of studies have been published recently on the association between ambient air pollution (AP) and incident diabetes mellitus (DM), but studies investigating source-specific AP toxicity and potential mediating pathways are rare. We investigated the associations of all-source, traffic-specific, and industry-specific outdoor AP exposure with 10-year incidence of DM and potential mediation via inflammation-associated biomarkers.

METHODS

Data from participants of the prospective Heinz Nixdorf Recall cohort study who attended the baseline (t₀; 2000-2003), 5-year follow-up (t₁; 2006-2008), and 10-year follow-up (t₂; 2011-2015) examinations was used. For participants without DM at baseline (determined using information on physician diagnosis and glucose-lowering medication), residential long-term exposure (total, traffic-specific, and industry-specific) to particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂), and accumulation mode particle number concentration (PN_AM) were estimated using a chemistry transport model. Covariate-adjusted modified Poisson regression models with robust standard errors were applied to estimate relative risks (RR) for the associations between baseline AP and incident DM at t₂. Mediation analyses for adiponectin, high-sensitivity C-reactive protein (hsCRP), and interleukin-1 receptor antagonist (IL-1RA) were conducted to estimate natural direct and indirect effects.

RESULTS

Of the 4,814 participants at t₀, 2,451 participants (mean baseline age: 58.2 years) were included in the main analysis. Interquartile range (IQR) increases in total PM₁₀ and PN_AM were associated with increased risk of DM (e.g., RR: 1.25 [95% Confidence Interval (CI): 1.02, 1.53] per 3.8 µg/m³ PM₁₀). Whereas traffic-specific exposures were associated with DM risk for all air pollutants (e.g., RR: 1.24 [95% CI: 1.06, 1.46] per 0.3 µg/m³ PM₁₀), significant associations for industry exposures were limited to NO₂ and PN_AM (e.g., RR: 1.24 [95% CI: 1.03, 1.49] per 230 particles/mL PN_AM). Potential mediation of the association between AP and DM was observed for adiponectin but not for hsCRP and IL-1RA.

CONCLUSION

Our study shows that long-term exposure to total and source-specific ambient AP may increase DM risk, with consistent results observed across traffic-specific exposures. Decreases in adiponectin may play a potential role along the causal pathway.

Association between environmental quality and diabetes in the USA

AIMS/INTRODUCTION

Caloric excess and physical inactivity fail to fully account for the rise of diabetes prevalence. Individual environmental pollutants can disrupt glucose homeostasis and promote metabolic dysfunction. However, the impact of cumulative exposures on diabetes risk is unknown.

MATERIALS AND METHODS

The Environmental Quality Index, a county-level index composed of five domains, was developed to capture the multifactorial ambient environmental exposures. The Environmental Quality Index was linked to county-level annual age-adjusted population-based estimates of diabetes prevalence rates. Prevalence differences (PD, annual difference per 100,000 persons) and 95% confidence intervals (CI) were estimated using random intercept mixed effects linear regression models. Associations were assessed for overall environmental quality and domain-specific indices, and all analyses were stratified by four rural-urban strata.

RESULTS

Comparing counties in the highest quintile/poorest environmental quality to those in the lowest quintile/best environmental quality, counties with poor environmental quality demonstrated lower total diabetes prevalence rates. Associations varied by rural-urban strata; overall better environmental quality was associated with lower total diabetes prevalence rates in the less urbanized and thinly populated strata. When considering all counties, good sociodemographic environments were associated with lower total diabetes prevalence rates (prevalence difference 2.77, 95% confidence interval 2.71-2.83), suggesting that counties with poor sociodemographic environments have an annual prevalence rate 2.77 per 100,000 persons higher than counties with good sociodemographic environments.

CONCLUSIONS

Increasing attention has focused on environmental exposures as contributors to diabetes pathogenesis, and the present findings suggest that comprehensive approaches to diabetes prevention must include interventions to improve environmental quality.

Benzene Exposure Induces Insulin Resistance in Mice

Benzene is a ubiquitous pollutant associated with hematotoxicity but its metabolic effects are unknown. We sought to determine if and how exposure to volatile benzene impacted glucose handling. We exposed wild type C57BL/6 mice to volatile benzene (50 ppm × 6 h/day) or HEPA-filtered air for 2 or 6 weeks and measured indices of oxidative stress, inflammation, and insulin signaling. Compared with air controls, we found that mice inhaling benzene demonstrated increased plasma glucose (p = .05), insulin (p = .03), and HOMA-IR (p = .05), establishing a state of insulin and glucose intolerance. Moreover, insulin-stimulated Akt phosphorylation was diminished in the liver (p = .001) and skeletal muscle (p = .001) of benzene-exposed mice, accompanied by increases in oxidative stress and Nf-κb phosphorylation (p = .025). Benzene-exposed mice also demonstrated elevated levels of Mip1-α transcripts and Socs1 (p = .001), but lower levels of Irs-2 tyrosine phosphorylation (p = .0001). Treatment with the superoxide dismutase mimetic, TEMPOL, reversed benzene-induced effects on oxidative stress, Nf-κb phosphorylation, Socs1 expression, Irs-2 tyrosine phosphorylation, and systemic glucose intolerance. These findings suggest that exposure to benzene induces insulin resistance and that this may be a sensitive indicator of inhaled benzene toxicity. Persistent ambient benzene exposure may be a heretofore unrecognized contributor to the global human epidemics of diabetes and cardiovascular disease.

Effects of long-term exposure to air pollution on the incidence of type 2 diabetes mellitus: a meta-analysis of cohort studies

This meta-analysis aimed to comprehensively assess the effects of long-term air pollution exposure on the risk of type 2 diabetes mellitus (T2DM). Studies were selected from three electronic databases. Random- or fixed-effect model was used to obtain the pooled hazard ratios (HRs) and corresponding 95% confidential intervals (CIs). Stratified analyses by regions of the studies and length of follow-up were conducted to assess the effects in different subgroups. Sensitivity analyses by omitted studies one by one, as well as adjusting certain confounding factors, were also conducted. The search resulted in 1878 studies, among which 16 studies with 18 cohorts were included. The incidence of T2DM was significantly associated with 10 μg/m³ increase of PM_2.5 (overall HR = 1.11, 95% CI: 1.03, 1.19) and PM₁₀ (overall HR = 1.12, 95% CI: 1.01, 1.23) exposure. Stratified analyses confirmed that PM_2.5 was significantly associated with increased T2DM incidence in American countries but not European countries. The results in the long follow-up subgroup also confirmed that exposure of PM_2.5 and PM₁₀ was associated with increased T2DM incidence. Interestingly, educational level and gender could potentially affect the impacts of PM₁₀ and PM_2.5 on T2DM incidence. The findings show long-term exposure to PM_2.5, and PM₁₀ could significantly increase the incidence of T2DM, especially in cohorts with long follow-up time.

The impact of air pollution on the incidence of diabetes and survival among prevalent diabetes cases

PURPOSE

Growing evidence implicates ambient air pollutants in the development of major chronic diseases and premature mortality. However, epidemiologic evidence linking air pollution to diabetes remains inconclusive. This study sought to determine the relationships between selected air pollutants (nitrogen dioxide [NO₂], fine particulate matter [PM_2.5], ozone [O₃], and oxidant capacity [Ox; the redox-weighted average of O₃ and NO₂]) and the incidence of diabetes, as well as the risk of cardiovascular or diabetes mortality among individuals with prevalent diabetes.

RESEARCH DESIGN AND METHODS

We followed two cohorts, which included 4.8 million Ontario adults free of diabetes and 452,590 Ontario adults with prevalent diabetes, from 2001 to 2015. Area-level air pollution exposures were assigned to subjects' residential areas, and outcomes were ascertained using health administrative data with validated algorithms. We estimated hazard ratios for the association between each air pollutant and outcome using Cox proportional hazards models, and modelled the shape of the concentration-response relationships.

RESULTS

Over the study period, 790,461 individuals were diagnosed with diabetes. Among those with prevalent diabetes, 26,653 died from diabetes and 64,773 died from cardiovascular diseases. For incident diabetes, each IQR increase in NO₂ had a hazard ratio of 1.04 (95% CI: 1.03-1.05). This relationship was relatively robust to all sensitivity analyses considered, and exhibited a near-linear shape. There were also positive associations between incident diabetes and PM_2.5, O₃, and Ox, but these estimates were somewhat sensitive to different models considered. Among those with prevalent diabetes, almost all pollutants were associated with increased diabetes and cardiovascular mortality risk. The strongest association was observed between diabetes mortality and exposure to NO₂ (HR = 1.08, 95% CI: 1.02-1.13).

CONCLUSIONS

Selected air pollutants, especially NO₂, were linked to an increased risk of incident diabetes, as well as risk of cardiovascular or diabetes mortality among persons with prevalent diabetes. As NO₂ is frequently used as a proxy for road traffic exposures, this result may indicate that traffic-related air pollution has the strongest effect on diabetes etiology and survival after diabetes development.

Risk Analysis of Air Pollution and Meteorological Factors Affecting the Incidence of Diabetes in the Elderly Population in Northern China

BACKGROUND

Research investigating the effect of air pollution on diabetes incidence is mostly conducted in Europe and the United States and often produces conflicting results. The link between meteorological factors and diabetes incidence remains to be explored. We aimed to explore associations between air pollution and diabetes incidence and to estimate the nonlinear and lag effects of meteorological factors on diabetes incidence.

METHODS

Our study included 19,000 people aged ≥60 years from the Binhai New District without diabetes at baseline. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) were used to explore the effect of air pollutants and meteorological factors on the incidence of diabetes. In the model combining the GAM and DLNM, the impact of each factor (delayed by 30 days) was first observed separately to select statistically significant factors, which were then incorporated into the final multivariate model. The association between air pollution and the incidence of diabetes was assessed in subgroups based on age, sex, and body mass index (BMI).

RESULTS

We found that cumulative RRs for diabetes incidence were 1.026 (1.011-1.040), 1.019 (1.012-1.026), and 1.051 (1.019-1.083) per 10 μg/m³ increase in PM_2.5, PM₁₀, and NO₂, respectively, as well as 1.156 (1.058-1.264) per 1 mg/m³ increase in CO in a single-pollutant model. Increased temperature, excessive humidity or dryness, and shortened sunshine duration were positively correlated with the incidence of diabetes in single-factor models. After adjusting for temperature, humidity, and sunshine, the risk of diabetes increased by 9.2% (95% confidence interval (CI):2.1%-16.8%) per 10 μg/m³ increase in PM_2.5. We also found that women, the elderly (≥75 years), and obese subjects were more susceptible to the effect of PM_2.5.

CONCLUSION

Our data suggest that PM_2.5 is positively correlated with the incidence of diabetes in the elderly, and the relationship between various meteorological factors and diabetes in the elderly is nonlinear.

Air pollution, noise exposure, and metabolic syndrome - A cohort study in elderly Mexican-Americans in Sacramento area

BACKGROUND

Previous studies suggested that air pollutants may increase the incidence of metabolic syndrome, but the potential impact from traffic sources is not well-understood. This study aimed to investigate associations between traffic-related nitrogen oxides (NOx) or noise pollution and risk of incident metabolic syndrome and its components in an elderly Mexican-American population.

METHODS

A total of 1,554 Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) cohort were followed from 1998 to 2007. We used anthropometric measures and biomarkers to define metabolic syndrome according to the recommendations of the Third Adult Treatment Panel of the National Cholesterol Education Program (NCEP ATP III). Based on participants' residential addresses at baseline, estimates of local traffic-related NOx were generated using the California Line Source Dispersion Model version 4 (CALINE4), and of noise employing the SoundPLAN software package. We used Cox regression models with calendar time as the underlying time scale to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations of air pollution or noise with metabolic syndrome or its components.

RESULTS

Each per unit increase of traffic-related NOx (2.29 parts per billion (ppb)) was associated with a 15% (HR = 1.15, 95% CI: 1.04-1.28) lower level of high-density lipoprotein cholesterol (HDL-cholesterol), and each 11.6 decibels (dB) increase in noise increased the risk of developing metabolic syndrome by 17% (HR = 1.17, 95% CI: 1.01-1.35).

CONCLUSION

Policies aiming to reduce traffic-related air pollution and noise might mitigate the risk of metabolic syndrome and its components in vulnerable populations.

Prenatal exposure to diesel exhaust PM2.5 programmed non-alcoholic fatty liver disease differently in adult male offspring of mice fed normal chow and a high-fat diet

Air pollution is one of the leading preventable threats to public health. Emerging evidence indicates that exposure to environmental stressors is associated with abnormal foetal development. However, how prenatal exposure to diesel exhaust PM_2.5 (DEP) predisposes adult offspring to the development of non-alcoholic fatty liver disease (NAFLD) remains unclear. To examine this, C57BL/6J mice were exposed to DEP or a vehicle before conception and during pregnancy and fed normal chow or a high-fat diet. Then, the hepatic fatty accumulation in the adult male offspring and possible molecular mechanisms were assessed. Our data showed that prenatal exposure to DEP on normal chow led to hepatic steatosis in adult male offspring with normal liver function. However, prenatal DEP exposure relieved the hepatic steatosis and liver function in offspring of mice fed a high-fat diet. Furthermore, prenatal exposure to DEP on normal chow increased lipogenesis and worsened fatty acid oxidation. The counteractive effect of prenatal DEP exposure on high-fat-diet-induced hepatic steatosis was produced through upregulated adenosine 5'-monophosphate-activated protein kinase, and this improved lipogenesis and fatty acid oxidation. Collectively, prenatal exposure to DEP programmed the development of NAFLD differently in the adult male offspring of mice fed normal chow and a high-fat diet, showing the pleotrophic effects of exposure to adverse environmental factors in early life.

Interaction between neighborhood walkability and traffic-related air pollution on hypertension and diabetes: The CANHEART cohort

BACKGROUND

Living in unwalkable neighborhoods has been associated with heightened risk for diabetes and hypertension. However, highly walkable environments may have higher concentrations of traffic-related air pollution, which may contribute to increased cardiovascular disease risk. We therefore aimed to assess how walkability and traffic-related air pollution jointly affect risk for hypertension and diabetes.

METHODS

We used a cross-sectional, population-based sample of individuals aged 40-74 years residing in selected large urban centres in Ontario, Canada on January 1, 2008, assembled from administrative databases. Walkability and traffic-related air pollution (NO₂) were assessed using validated tools and linked to individuals based on neighborhood of residence. Logistic regression was used to estimate adjusted associations between exposures and diagnoses of hypertension or diabetes accounting for potential confounders.

RESULTS

Overall, 2,496,458 individuals were included in our analyses. Low walkability was associated with higher odds of hypertension (lowest vs. highest quintile OR = 1.34, 95% CI: 1.32, 1.37) and diabetes (lowest vs. highest quintile OR = 1.25, 95% CI: 1.22, 1.29), while NO₂ exhibited similar trends (hypertension: OR = 1.09 per 10 p.p.b., 95% CI: 1.08, 1.10; diabetes: OR = 1.16, 95% CI: 1.14, 1.17). Significant interactions were identified between walkability and NO₂ on risk for hypertension (p < 0.0001 and diabetes (p < 0.0001). At higher levels of pollution (40 p.p.b.), differences in the probability of hypertension (lowest vs. highest walkability quintile: 0.26 vs. 0.25) or diabetes (lowest vs. highest walkability quintile: 0.15 vs. 0.15) between highly walkable and unwalkable neighborhoods were diminished, compared to differences observed at lower levels of pollution (5 p.p.b.) (hypertension, lowest vs. highest walkability quintile: 0.21 vs. 0.13; diabetes, lowest vs. highest walkability quintile: 0.09 vs. 0.06).

CONCLUSIONS

Walkability and traffic-related air pollution interact to jointly predict risk for hypertension and diabetes. Although walkable neighborhoods appear to have beneficial effects, they may accentuate the harmful effects of air pollution on cardiovascular risk factors.

The mediated role of complement C3 in PM2.5 exposure and type 2 diabetes mellitus: an elderly panel study in Beijing, China

Diabetes mellitus (DM) is a common chronic disease worldwide. Ambient air pollution has long been proven to be associated with type 2 diabetes mellitus (T2DM) progression, but the underlying mechanism is not clear yet. In addition, previous studies mainly focused on the prevention of healthy people against the incidence of T2DM. We designed a panel study including two follow-ups and enrolled 39 patients with T2DM living in Beijing. Linear mixed model was fitted to assess the association between two pairs of variables (ambient air pollution exposure and C3 levels, ambient air pollution exposures and T2DM index). Mediation analysis of C3 between ambient air pollution exposure and indicators of T2DM progression was conducted. We found that PM2.5 exposures is are negatively associated with serum complement C3. Given that C3 might act as a protector of pancreas β cell, PM2.5 exposures could accelerate disease in T2DM populations. No mediation effects were found. This study reveals that exposures to PM2.5 can cause progression of diseases among T2DM populations.

Evaluating deciduous tree leaves as biomonitors for ambient particulate matter pollution in Pittsburgh, PA, USA

Fine particulate matter (PM_2.5) air pollution varies spatially and temporally in concentration and composition and has been shown to cause or exacerbate adverse effects on human and ecological health. Biomonitoring using airborne tree leaf deposition as a proxy for particulate matter (PM) pollution has been explored using a variety of study designs, tree species, sampling strategies, and analytical methods. In the USA, relatively few have applied these methods using co-located fine particulate measurements for comparison and relying on one tree species with extensive spatial coverage, to capture spatial variation in ambient air pollution across an urban area. Here, we evaluate the utility of this approach, using a spatial saturation design and pairing tree leaf samples with filter-based PM_2.5 across Pittsburgh, Pennsylvania, with the goal of distinguishing mobile and stationary sources using PM_2.5 composition. Co-located filter and leaf-based measurements revealed some significant associations with traffic and roadway proximity indicators. We compared filter and leaf samples with differing protection from the elements (e.g., meteorology) and PM collection time, which may account for some variance in PM source and/or particle size capture between samples. To our knowledge, this study is among the first to use deciduous tree leaves from a single tree species as biomonitors for urban PM_2.5 pollution in the northeastern USA.

A meta-analysis of selected near-road air pollutants based on concentration decay rates

Traffic-related air pollution has been associated with various health risks for human populations living near roadways. Understanding the relationship between traffic density and dispersion of vehicle-released air pollutants is important for assessing human exposure to near-road air pollutants. We performed a literature survey targeting publications containing measurement data of traffic-related air pollutants near roads with distance information on their concentration distribution. Concentration decay rates over down-wind distance away from major roads were calculated for black carbon (BC), carbon monoxide (CO) and nitrogen oxides (NO₂ or NO_x) and meta-data analysis on these rates was performed. These analyses showed metadata-based exponential decay rates of 0.0026, 0.0019, 0.0004, and 0.0027 m⁻¹ for BC, CO, NO₂ and NOx, respectively. Using these measurement data-based decay rates, concentrations for BC, CO, NO₂ and NO_x over various near-road distances were predicted. These results are useful for enhancing exposure modeling and thus more reliably assessing the health risk of exposure to near road air pollution.

Associations of ambient fine particulate matter and its constituents with serum complement C3 in a panel study of older adults in China

Epidemiological studies have demonstrated association between the total mass of fine particulate matter (PM_2.5) exposures and inflammation. There are few studies exploring the associations between PM_2.5 constituents and the biomarkers of inflammation in older adults and the underlying biological mechanisms are not exact. In this study, we examined the associations between PM_2.5 and its constituents (organic carbon (OC), elemental carbon (EC), total carbon (TC), polycyclic aromatic hydrocarbons (PAHs) and complement three factor (C3), an important biomarker of inflammation in a repeated panel of 175 older adults in Beijing, China. We have constructed three different linear mixed effect models (single-pollutant model, constituent-PM_2.5 joint model, and constituent-residual model) to evaluate the association of PM_2.5 and its constituents and complement C3, controlling for concentration of high sensitive C-reactive protein (hs-CRP), day of week, mean temperature, relative humidity, location and potential individual confounders. We found robust positive associations of OC, EC, TC, PAHs and PM_2.5 mass concentration with complement C3 at different lag patterns. The cumulative effects of pollutants increased across average of 2-5 days. Individuals aged 65 and above, or with diabetes, or BMI ≥30, or with no-cardiopathy, or with hypertension also exhibited positive associations between PM_2.5 and complement C3. The results revealed that short-term exposure to PM_2.5 and its constituents could result in a significant increase in serum level of complement C3. These findings suggested a possible involvement of complement C3 in the effect of PM_2.5 on inflammatory reaction.

Susceptibility of prediabetes to the health effect of air pollution: a community-based panel study with a nested case-control design

BACKGROUND

Recent studies suggest that people with diabetes or who are at risk of developing diabetes, i.e. prediabetic (preDM), are potentially susceptible to air pollution, but the underlying mechanisms remain unclear because the existing epidemiological studies did not include healthy control groups and only focused on limited health outcomes. We hypothesized that acute exposure to ambient fine particles (PM_2.5) will lead to enhanced pulmonary and cardiometabolic changes in preDM than healthy individuals.

METHODS

We recruited 60 preDM and 60 healthy individuals from a community of 22,343 adults in Beijing China, and arranged each subject to complete up to seven repeated clinical visits with measures of 6 cardiopulmonary biomarkers, 6 cytokines, 4 blood pressure and endothelial function outcomes and 4 glucose metabolism biomarkers.. Moving averaged daily ambient PM_2.5 in preceding 1-14 days was matched to each subject and the PM_2.5 associated effect on multiple biomarkers was estimated and compared between PreDM and healthy subjects based on linear mixed effect model.

RESULTS

All the subjects exhibited significant acute elevation of exhaled nitric oxide, white blood cells, neutrophils, interleukin-1α, and glycated haemoglobin with increased exposure to PM_2.5. PreDM subjects had significant stronger adverse changes compared to healthy subjects in 6 cardiometabolic biomarkers, namely, interleukin-2, interleukin-8, systolic and diastolic blood pressure, augmentation pressure, and glucose. The maximum elevation of these 6 biomarkers in PreDM subjects were 8.6% [CI: 4.1-13.3%], 10.0% [CI: 3.9-16.4%], 1.9% [CI: 0.2-3.6%], 1.2% [CI: - 0.1-2.4%], 5.7% [CI: - 0.1-11.8%], 2.4% [CI: 0.7-4.2%], respectively, per an interquartile increase of ambient PM_2.5 (61.4 μg m^- 3) throughout the exposure window of the preceding 1-14 days. No significant difference was observed for the changes in pulmonary biomarkers between the two groups.

CONCLUSIONS

PreDM individuals are more susceptible to the acute cardiometabolic effect of air pollution than the healthy individuals. A considerable public health burden can be inferred, given the high prevalence of prediabetes and the ubiquity of air pollution in China and worldwide.

Bibliometric analysis of cardiometabolic disorders studies involving NO2, PM2.5 and noise exposure

BACKGROUND

This study uses bibliometric analysis to describe the state of research about the association of NO2, PM2.5 and noise exposures - three traffic-related pollutants - with cardiometabolic disorders.

METHODS

We retrieved references published 1994-2017 from Scopus and classified references with respect to exposure, health outcome and study design using index keywords. Temporal trend, top cited references, used index keywords and the number of hypothesis testing and non-hypothesis testing study design for each group were identified.

RESULTS

Results show PM2.5 is the most frequently studied exposure (47%), followed by both NO2 and PM2.5 exposure (29%). Only 3% of references considered multiple exposures between NO2 and/or PM2.5 and noise, and these were published after 2008. While we observed a growing trend in studies with NO2 and/or PM2.5 and noise and diabetes in the last decade, there is a diminishing trend in studies with noise and diabetes. Different patterns of study designs were found through H/NH ratio, the number of references classified as having a hypothesis (H)-testing design relative to the number of references classified as having a non-hypothesis (NH)-testing design. Studies with NO2 and/or PM2.5 exposure are more likely to have a H-testing design, while those with noise exposure are more likely to have a NH-testing design, such as cross-sectional study design.

CONCLUSIONS

We conclude with three themes about research trends. First, the study of simultaneous exposures to multiple pollutants is a current trend, and likely to continue. Second, the association between traffic-related pollutants and diabetes and metabolic symptoms is an area for growth in research. Third, the transition to the use of H-testing study designs to explore associations between noise and cardiometabolic outcomes may be supported by improved understanding of the mechanism of action, and/or improvements to the accuracy and precision of air pollution and noise exposure assessments for environmental health research.

Impact of environmental pollution, dietary factors and diabetes mellitus on Autism Spectrum Disorder (ASD)

Autism spectrum disorder (ASD) is complex neurodevelopmental condition described by impairments in three main behavioral areas: social deficits, impaired communication, and repetitive behaviors. Despite many years of vast study, the causes of ASD are still unknown. Various risk factors including genetic, infectious, metabolic and immunological have been investigated however, environmental, nutritional and diabetes related risk factors have not received sufficient attention. This study has provided an insight into the comprehensive interaction between environmental pollution, dietary factors and diabetes mellitus that could lead to the advancement of this debilitating neurodevelopment disorder. The literature search was done using PubMed and Google Scholar databases up to October 2018. Key words "Environmental Pollution", "Nutritional Factors", "Diabetes Mellitus", "Autism Spectrum Disorder" were selected.

Ambient air pollution and risk of type 2 diabetes in the Chinese

We performed a time series analysis to investigate the potential association between exposure to ambient air pollution and type 2 diabetes (T2D) incidence in the Chinese population. Monthly time series data between 2008 and 2015 on ambient air pollutants and incident T2D (N = 25,130) were obtained from the Environment Monitoring Center of Ningbo and the Chronic Disease Surveillance System of Ningbo. Relative risks (RRs) and 95% confidence intervals (95% CIs) of incident T2D per 10 μg/m³ increases in ambient air pollutants were estimated from Poisson generalized additive models. Exposure to particulate matter < 10 μm (PM₁₀) and sulfur dioxide (SO₂) was associated with increased T2D incidence. The relative risks (RRs) of each increment in 10 μg/m³ of PM₁₀ and SO₂ were 1.62 (95% CI, 1.16-2.28) and 1.63 (95% CI, 1.12-2.38) for overall participants, whereas for ozone (O₃) exposure, the RRs were 0.78 (95% CI, 0.68-0.90) for overall participants, 0.78 (95% CI, 0.69-0.90) for males, and 0.78 (95% CI, 0.67-0.91) for females, respectively. Exposure to PM₁₀ and SO₂ is positively associated with T2D incidence, whereas O₃ is negatively associated with T2D incidence.

Prevalence of Diabetes Mellitus and Exposure to Suspended Particulate Matter

BACKGROUND

Evidence from various epidemiological studies has shown an association between particulate matter 2.5 (PM2.5) and diabetes mellitus. A prospective study from the United States reported that exposure to PM2.5 alters endothelial function, and leads to insulin resistance and reduction in peripheral glucose uptake. There is a paucity of data on the relation between air pollution and diabetes in low- and middle-income countries.

OBJECTIVES

To estimate the prevalence of type 2 diabetes among people living in areas with higher exposures of suspended PM2.5 compared to people living in areas with lower exposures in Chennai, Tamil Nadu, India.

METHODS

A cross-sectional study was carried out in two areas of Chennai city. The PM2.5 affecting vulnerable areas were stratified from a list of air quality monitoring stations in Tamil Nadu Pollution Control Board and Central Pollution Control Board. The highest and lowest areas of exposure were selected from the list. Households were randomly selected for the study. A total of 201 (67 male, 134 female) individuals from a high exposure area (HEA) and 209 (76 male 133 female) individuals from a low exposure area (LEA) were recruited for the study. Adults over 18 years of age were screened for random capillary blood glucose (RCBG) by glucometer (OneTouch Ultra).

RESULTS

The prevalence of diabetes (34.8% vs 19.6% p =0.001) was 77.5% higher among people living in areas of high particulate matter exposure compared to people living in less exposed areas. Multivariable logistic regression analysis showed that age, gender, residential area, and family history of diabetes were significantly associated with the prevalence of diabetes (p<0.05).

CONCLUSIONS

The present study indicates a link between high levels of exposure to PM2.5 and diabetes mellitus. Further prospective studies on populations exposed to elevated pollution are needed to establish whether this association has a causative link.

PARTICIPANT CONSENT

Obtained.

ETHICS APPROVAL

The study was approved by the Ethics Committee of the Prof. M Viswanathan Diabetes Research Centre, Chennai, India.

COMPETING INTERESTS

The authors declare no competing financial interests.

Long-term exposure to ambient fine particulate matter (PM2.5) and incident type 2 diabetes: a longitudinal cohort study

AIMS/HYPOTHESIS

Information on the associations of long-term exposure to fine particulate matter (with an aerodynamic diameter less than 2.5 μm; PM_2.5) with the development of type 2 diabetes is scarce, especially for south-east Asia, where most countries are experiencing serious air pollution. This study aimed to investigate the long-term effects of exposure to ambient PM_2.5 on the incidence of type 2 diabetes in a population of Taiwanese adults.

METHODS

A total of 147,908 participants without diabetes, at least 18 years of age, were recruited in a standard medical examination programme between 2001 and 2014. They were encouraged to take medical examinations periodically and underwent at least two measurements of fasting plasma glucose (FPG). Incident type 2 diabetes was identified as FPG ≥7 mmol/l or self-reported physician-diagnosed diabetes in the subsequent medical visits. The PM_2.5 concentration at each participant's address was estimated using a satellite-based spatiotemporal model with a resolution of 1 × 1 km². The 2 year average of PM_2.5 concentrations (i.e. the year of and the year before the medical examination) was treated as an indicator of long-term exposure to ambient PM_2.5 air pollution. We performed Cox regression models with time-dependent covariates to analyse the long-term effects of exposure to PM_2.5 on the incidence of type 2 diabetes. A wide range of covariates were introduced in the models to control for potential effects, including age, sex, education, season, year, smoking status, alcohol drinking, physical activity, vegetable intake, fruit intake, occupational exposure, BMI, hypertension and dyslipidaemia (all were treated as time-dependent covariates except for sex).

RESULTS

Compared with the participants exposed to the first quartile of ambient PM_2.5, participants exposed to the second, third and fourth quartiles of ambient PM_2.5 had HRs of 1.28 (95% CI 1.18, 1.39), 1.27 (95% CI 1.17, 1.38) and 1.16 (95% CI 1.07, 1.26), respectively, for the incidence of type 2 diabetes. Participants who drank occasionally or regularly (more than once per week) or who had a lower BMI (<23 kg/m²) were more sensitive to the long-term effects of exposure to ambient PM_2.5.

CONCLUSIONS/INTERPRETATION

Long-term exposure to ambient PM_2.5 appears to be associated with a higher risk of developing type 2 diabetes in this Asian population experiencing high levels of air pollution.

Risks of hospital admissions from a spectrum of causes associated with particulate matter pollution

Ambient particulate matter (PM) pollution has been linked to elevated hospital admissions (HAs), especially from respiratory and cardiovascular diseases. However, few studies have estimated the associations between PM pollution and HAs for a wider range of broad disease categories. This study aimed to evaluate the effects of PM with aerodynamic diameter ≤ 2.5 μm (PM_2.5) and ≤10 μm (PM₁₀) on a range of broad and specific causes of HAs in Chengdu, China during 2015-2016, using a generalized additive model (GAM). Age-, gender- and season-specific analyses were also performed on the broad categories. We further calculated the corresponding morbidity burden due to PM exposure. During the study period, the daily mean level for PM_2.5 and PM₁₀ was 57.3 μg/m³ and 94.7 μg/m³, respectively. For broad disease categories, each 10 μg/m³ increase in PM₁₀ at lag06 was associated with increments of 0.65% (95% CI: 0.32%-0.99%) in HAs from respiratory, 0.49% (95% CI: 0.04%-0.95%) from circulatory and 0.91% (95% CI: 0.15%-1.69%) from skin and subcutaneous tissue diseases. By contrast, only respiratory HAs showed a significant positive association with elevated PM_2.5 at lag06 (1.03% increase per 10 μg/m³, 95% CI: 0.50%-1.56%, p < 0.001). Increased HAs risks for several more refined specific causes within respiratory, circulatory, skin and subcutaneous tissue, nervous and genitourinary diseases were also observed. Subgroup analyses indicated that effect estimates were modified by age, gender and season. Overall, the largest morbidity burden was observed in myocardial infarction, about 11.27% (95% CI: 3.45%-18.07%) and 11.11% (95% CI: 4.07%-17.27%) of HAs for myocardial infarction could be attributable to PM_2.5 and PM₁₀ levels exceeding the WHO's air quality guidelines (24-h mean: 25 μg/m³ for PM_2.5 and 50 μg/m³ for PM₁₀). Our study suggests that both PM_2.5 and PM₁₀ increase risks of morbidity from broad range of causes of HAs in Chengdu, and result in substantial morbidity burden.

Air pollution and diabetes-related biomarkers in non-diabetic adults: A pathway to impaired glucose metabolism?

BACKGROUND

While prior studies have linked air pollution (AP) to diabetes prevalence and incidence, few have investigated whether AP exposure is also associated with alterations in diabetes-related biomarkers in metabolically healthy adults.

OBJECTIVE

To evaluate the associations between short-, medium-, and long-term AP and diabetes-related biomarkers (adiponectin, interleukin-1 receptor antagonist [IL-1RA], high sensitivity C-reactive protein [hsCRP], fibrinogen) in persons without diabetes.

METHODS

Adiponectin, IL-1RA, hsCRP, and fibrinogen were measured in blood samples collected at the baseline (t₀; 2000-2003) and first follow-up (t₁; 2006-2008) examinations of the prospective Heinz Nixdorf Recall (HNR) cohort study in Germany. Participants' residential mean exposures to PM₁₀, PM_2.5, NO₂, and accumulation mode particle number concentration (PN_AM) were estimated for several time windows (1- to 365-day) prior to examination using a dispersion and chemistry transport model. We fitted covariate-adjusted linear mixed effects models using a random participant intercept and investigated effect modification by obesity status.

RESULTS

We analyzed 6727 observations (n_t0 = 3626, n_t1 = 3101) from 4052 participants of the HNR study (52% women; ages 45-76 years at t₀). For all air pollutants, medium-term exposures (60- to 120-day) were negatively associated with adiponectin (e.g., 91-day PN_AM: -2.51% change [-3.40%, -1.53%] per interquartile [IQR] increase). Several short-, medium-, and long-term AP exposures were positively associated with IL-1RA (e.g., 365-day PM₁₀: 2.64% change [1.25%, 4.22%] per IQR increase). Long-term exposures were positively associated with hsCRP level while no consistent patterns were observed for fibrinogen. Stronger associations for adiponectin were observed among non-obese participants.

CONCLUSION

In persons without diabetes, we observed differing patterns of association between AP and diabetes-related biomarkers across a range of exposure windows, supporting the hypothesis that AP may play a role in the development of diabetes.

Within-subject effects of environmental and social stressors on pre- and post-partum obesity-related biobehavioral responses in low-income Hispanic women: protocol of an intensive longitudinal study

BACKGROUND

Disproportionately high rates of maternal overweight and obesity among the Hispanic population before, during, and after pregnancy pose serious health concerns for both mothers (e.g., preeclampsia, gestational diabetes, weight retention) and children (e.g., elevated lifelong obesity risk). A growing body of evidence implicates environmental exposures (e.g., air pollution, metals) and social stressors (e.g., poverty, violence) in contributing to obesity-related biobehavioral processes, such as physical activity, dietary intake, perceived stress, and cortisol regulation. However, current understanding of the role of environmental exposures and social stressors on obesity-related biobehavioral processes is limited by infrequent, inter-individual measurement, and lack of personal exposure monitoring.

METHODS

The "Maternal and Developmental Risks from Environmental and Social Stressors" (MADRES) real-time and personal sampling study examines the within-subject day-level effects of environmental and social stressors on maternal pre- and post-partum obesity-related biobehavioral responses. Among a cohort of 65 low-income, Hispanic women in urban Los Angeles, this study uses innovative personal, real-time data capture strategies (e.g., ecological momentary assessment [EMA], personal exposure monitoring, geolocation monitoring, accelerometry) to repeatedly assess obesity-related processes during the 1st and 3rd trimester, and at 4-6 months postpartum. Day-level effects of environmental exposures and social stressors on women's physical activity, diet, perceived stress and salivary cortisol measured across repeated days will be tested using multilevel modeling.

DISCUSSION

Hispanic women of childbearing age bear a disproportionately high burden of obesity, and this population is also unduly exposed to numerous obesogenic settings. By using innovative real-time data capture strategies, the current study will uncover the daily impacts of environmental and social stressor exposures on women's obesity-related biobehavioral responses, which over time can lead to excessive gestational weight gain, postpartum weight retention and can pose serious consequences for both mother and child. Findings from the real-time and personal sampling study will identify key mechanistic targets for policy, clinical, and programmatic interventions, with the potential for broad-reaching public health impacts.

Neighborhood sociodemographic effects on the associations between long-term PM2.5 exposure and cardiovascular outcomes and diabetes

Supplemental Digital Content is available in the text.

Exposure to PM_2.5 air pollution and neighborhood-level sociodemographic characteristics are associated with cardiovascular disease and possibly diabetes mellitus. However, the joint effect of sociodemographics and PM_2.5 on these outcomes is uncertain.