Epidemiology: air pollution and mortality from diabetes mellitus

Influence of Air Pollution Exposures on Cardiometabolic Risk Factors: a Review

PURPOSE OF REVIEW

The increasing prevalence of cardiometabolic risk factors (CRFs) contributes to the rise in cardiovascular disease. Previous research has established a connection between air pollution and both the development and severity of CRFs. Given the ongoing impact of air pollution on human health, this review aims to summarize the latest research findings and provide an overview of the relationship between different types of air pollutants and CRFs.

RECENT FINDINGS

CRFs include health conditions like diabetes, obesity, hypertension etc. Air pollution poses significant health risks and encompasses a wide range of pollutant types, air pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O2). More and more population epidemiological studies have shown a positive correlation between air pollution and CRFs. Although various pollutants have diverse effects on specific cellular molecular pathways, their main influence is on oxidative stress, inflammation response, and impairment of endothelial function. More and more studies have proved that air pollution can promote the occurrence and development of cardiovascular and metabolic risk factors, and the research on the relationship between air pollution and CRFs has grown intensively. An increasing number of studies are using new biological monitoring indicators to assess the occurrence and development of CRFs resulting from exposure to air pollution. Abnormalities in some important biomarkers in the population (such as homocysteine, uric acid, and C-reactive protein) caused by air pollution deserve more attention. Further research is warranted to more fully understand the link between air pollution and novel CRF biomarkers and to investigate potential prevention and interventions that leverage the mechanistic link between air pollution and CRFs.

Diabetes and climate change

It is widely accepted that climate change is the biggest threat to human health. The pandemic of diabetes is also a major threat to human health, especially in rapidly developing nations. Climate change and diabetes appear to have common global vectors, including increased urbanisation, increased use of transportation, and production and ingestion of ultra-processed foods. People with diabetes appear to be at higher risk of threats to health from climate change, including effects from extreme heat or extreme cold, and natural disasters. Solutions to climate change offer some benefits for the prevention of diabetes and diabetes-related complications. Moving towards lower carbon economies is likely to help reduce reliance on intensive agriculture, reduce physical inactivity, reduce air pollution and enhance quality of life. It may enable a reduction in the prevalence of diabetes and reduced morbidity from the condition.

Engaging communities in addressing air quality: a scoping review

BACKGROUND

Exposure to air pollution has a detrimental effect on health and disproportionately affects people living in socio-economically disadvantaged areas. Engaging with communities to identify concerns and solutions could support organisations responsible for air quality control, improve environmental decision-making, and widen understanding of air quality issues associated with health. This scoping review aimed to provide an overview of approaches used to engage communities in addressing air quality and identify the outcomes that have been achieved.

METHODS

Searches for studies that described community engagement in air quality activities were conducted across five databases (Academic Search Complete, CABI, GreenFILE, MEDLINE, Web of Science). Data on study characteristics, community engagement approach, and relevant outcomes were extracted. The review process was informed by a multi-stakeholder group with an interest in and experience of community engagement in air quality. Thirty-nine papers from thirty studies were included in the final synthesis.

CONCLUSION

A range of approaches have been used to engage communities in addressing air quality, most notably air quality monitoring. Positive outcomes included increased awareness, capacity building, and changes to organisational policy and practice. Longer-term projects and further exploration of the impact of community engagement on improving air quality and health are needed as reporting on these outcomes was limited.

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

A Novel Rabbit Dry Eye Model Induced by a Controlled Drying System

Purpose

To establish an environment-induced dry eye model in rabbits using a controlled drying system (CDS).

Methods

Rabbits were randomly divided into two groups. The rabbits in the dry group were housed in the CDS, in which the relative humidity, airflow, and temperature were controlled at 22% ± 4%, 3 to 4 m/s, and 23°C to 25°C for 14 days. The rabbits in the control group were housed in a normal environment at the same time. A Schirmer test, fluorescein staining, and lissamine green staining were performed. On day 14, the eyeballs and lacrimal glands were processed for evaluating the corneal epithelial thickness, inflammatory cell infiltration index, goblet cell density, and expression of the MUC5AC protein and caspase-3 protein. The mRNA expression of the involved inflammatory genes was analyzed.

Results

The CDS was able to maintain a dry environment, in which the tear production decreased, and the ocular surface staining increased over time in the rabbits. In the dry group, the corneal epithelium became thinner, inflammatory cells were noted, goblet cells and MUC5AC proteins decreased, and the increased levels of caspase-3 proteins and inflammatory cytokines were observed in the ocular surface tissues and lacrimal glands.

Conclusions

This CDS could create a dry environment, in which the rabbits exhibited a pathological change in dry eye similar to that in humans.

Translational Relevance

This model would be helpful in offering a platform to identify and test candidate therapies for environment-induced dry eye and to explore its underlying mechanisms.

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 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.

Soluble ST2 is associated with increased carotid intima-media thickness in patients with type 2 diabetes mellitus: A case-control study

Soluble suppression of tumorigenicity 2 (sST2) is a free form of membrane-bound ST2, which is a member of the interleukin-1 receptor family. Previous research has shown that sST2 is associated with diabetes, but cardiovascular risk factors have not been established.To analyze the relationship between sST2 and carotid intima-media thickness (CIMT) in patients with type 2 diabetes mellitus (T2DM).After screening, a total of 118 subjects with T2DM were divided into 2 groups according to the measurement of CIMT (normal CIMT (NCIMT), n = 58; abnormal CIMT (ACIMT), n = 60), and 60 healthy subjects (normal control (NC), n = 60) were recruited in this study. CIMT was measured by a color Doppler ultrasound, and sST2 and other metabolic parameters were measured as well.The median concentration of sST2 was elevated in the ACIMT group (31.30 ng/ml) compared with the NCIMT group (28.29 ng/ml, P < .01) and the NC group (20.15 ng/ml, P < .01). After adjustment for age and sex, log sST2 was strongly associated with smoking history (β = 0.197, 95% CI, 0.084-0.311, P < .01), FPG level (β = 0.302, 95% CI, 0.162-0.442, P < .01) and HbA1c level (β = 0.296, 95% CI, 0.165-0.426, P < .01) and negatively correlated with HDL level (β = -0.153, 95% CI, -0.259 to -0.046, P < .01). Furthermore, sST2 level was a risk factor for increased CIMT in patients with T2DM.Increased sST2 level not only was associated with indicators of glucose and lipid metabolism but also was a risk factor for increased CIMT in patients with T2DM. Thus, sST2 may be a potential novel marker to assess the progression of diabetic macrovascular complications.

Associations of Exposure to Air Pollution with Insulin Resistance: A Systematic Review and Meta-Analysis

In this article, we review the available evidence and explore the association between air pollution and insulin resistance (IR) using meta-analytic techniques. Cohort studies published before January 2018 were selected through English-language literature searches in nine databases. Six cohort studies were included in our sample, which assessed air pollutants including PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm), NO₂(nitrogen dioxide), and PM10 (particulate matter with an aerodynamic diameter less than 10 μm). Percentage change in insulin or insulin resistance associated with air pollutants with corresponding 95% confidence interval (CI) was used to evaluate the risk. A pooled effect (percentage change) was observed, with a 1 μg/m³ increase in NO₂ associated with a significant 1.25% change (95% CI: 0.67, 1.84; I² = 0.00%, p = 0.07) in the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and a 0.60% change (95% CI: 0.17, 1.03; I² = 30.94%, p = 0.27) in insulin. Similar to the analysis of NO₂, a 1 μg/m³ increase in PM10 was associated with a significant 2.77% change (95% CI: 0.67, 4.87; I² = 94.98%, p < 0.0001) in HOMA-IR and a 2.75% change in insulin (95% CI: 0.45, 5.04; I² = 58.66%, p = 0.057). No significant associations were found between PM2.5 and insulin resistance biomarkers. We conclude that increased exposure to air pollution can lead to insulin resistance, further leading to diabetes and cardiometabolic diseases. Clinicians should consider the environmental exposure of patients when making screening and treatment decisions for them.

Association Between Long-term Exposure to Air Pollution and Biomarkers Related to Insulin Resistance, Subclinical Inflammation, and Adipokines

Insulin resistance (IR) is present long before the onset of type 2 diabetes and results not only from inherited and lifestyle factors but also likely from environmental conditions. We investigated the association between modeled long-term exposure to air pollution at residence and biomarkers related to IR, subclinical inflammation, and adipokines. Data were based on 2,944 participants of the KORA (Cooperative Health Research in the Region Augsburg) F4 study conducted in southern Germany (2006-2008). We analyzed associations between individual air pollution concentration estimated by land use regression and HOMA-IR, glucose, insulin, HbA_1c, leptin, and high-sensitivity C-reactive protein levels from fasting samples using multivariable linear regression models. Effect estimates were calculated for the whole study population and subgroups of individuals who did not have diabetes, had prediabetes, or had diabetes. Among all participants, a 7.9 μg/m³ increment in particulate matter of <10 μm was associated with higher HOMA-IR (15.6% [95% CI 4.0; 28.6]) and insulin (14.5% [3.6; 26.5]). Nitrogen dioxide was associated with HOMA-IR, glucose, insulin, and leptin. Effect estimates for individuals with prediabetes were much larger and highly statistically significant, whereas individuals who did not have diabetes or had diabetes showed rather weak associations. No association was seen for HbA_1c level. Our results suggested an association between long-term exposure to air pollution and IR in the general population that was attributable mainly to individuals with prediabetes.

Distribution and clinical association of plasma soluble ST2 during the development of type 2 diabetes

AIM

ST2 plays important roles in diabetes and cardiovascular diseases. However, the distribution and changes in plasma soluble ST2 during the development of type 2 diabetes remain unclear.

METHODS

In the present study, 525 subjects were recruited and divided into three groups: normal, prediabetic and diabetic subjects. The sST2 levels of all subjects were measured using a high-sensitivity assay.

RESULTS

sST2 levels were modestly but significantly elevated in patients with diabetes (26.1ng/ml) compared with normal subjects (19.3ng/ml, P<0.001) and persons with prediabetes (20.3ng/ml, P<0.001). The third and fourth quartiles (21.3 and 29.1ng/ml, respectively) of the sST2 levels were associated with a 2.31- and 4.00-fold increased risk of having diabetes. With the prediabetic group as a reference population, patients with sST2 levels in the fourth quartiles had a higher increased risk of having diabetes mellitus (odds ratios=2.19, P<0.05). Furthermore, each SD log sST2 was associated with a 1.57-fold increased risk of atherosclerosis when all relevant variables was added to the multivariable logistic regression models. After adjustment for age and sex, all markers of liver and renal function, HDL-cholesterol, total cholesterol and smoking status showed a significant association with sST2 levels.

CONCLUSION

Elevated sST2 levels were not only associated with metabolic characteristics of diabetes but also with a significantly increased risk of having diabetes.

Genome-Wide Analysis of DNA Methylation and Fine Particulate Matter Air Pollution in Three Study Populations: KORA F3, KORA F4, and the Normative Aging Study

BACKGROUND

Epidemiological studies have reported associations between particulate matter (PM) concentrations and cancer and respiratory and cardiovascular diseases. DNA methylation has been identified as a possible link but so far it has only been analyzed in candidate sites.

OBJECTIVES

We studied the association between DNA methylation and short- and mid-term air pollution exposure using genome-wide data and identified potential biological pathways for additional investigation.

METHODS

We collected whole blood samples from three independent studies-KORA F3 (2004-2005) and F4 (2006-2008) in Germany, and the Normative Aging Study (1999-2007) in the United States-and measured genome-wide DNA methylation proportions with the Illumina 450k BeadChip. PM concentration was measured daily at fixed monitoring stations and three different trailing averages were considered and regressed against DNA methylation: 2-day, 7-day and 28-day. Meta-analysis was performed to pool the study-specific results.

RESULTS

Random-effect meta-analysis revealed 12 CpG (cytosine-guanine dinucleotide) sites as associated with PM concentration (1 for 2-day average, 1 for 7-day, and 10 for 28-day) at a genome-wide Bonferroni significance level (p ≤ 7.5E-8); 9 out of these 12 sites expressed increased methylation. Through estimation of I2 for homogeneity assessment across the studies, 4 of these sites (annotated in NSMAF, C1orf212, MSGN1, NXN) showed p > 0.05 and I2 < 0.5: the site from the 7-day average results and 3 for the 28-day average. Applying false discovery rate, p-value < 0.05 was observed in 8 and 1,819 additional CpGs at 7- and 28-day average PM2.5 exposure respectively.

CONCLUSION

The PM-related CpG sites found in our study suggest novel plausible systemic pathways linking ambient PM exposure to adverse health effect through variations in DNA methylation.

CITATION

Panni T, Mehta AJ, Schwartz JD, Baccarelli AA, Just AC, Wolf K, Wahl S, Cyrys J, Kunze S, Strauch K, Waldenberger M, Peters A. 2016. A genome-wide analysis of DNA methylation and fine particulate matter air pollution in three study populations: KORA F3, KORA F4, and the Normative Aging Study. Environ Health Perspect 124:983-990; http://dx.doi.org/10.1289/ehp.1509966.

Minimizing Air Pollution Exposure: A Practical Policy to Protect Vulnerable Older Adults from Death and Disability

Air pollution causes an estimated 200,000 deaths per year in the United States alone. Older adults are at greater risk of mortality caused by air pollution. Here we quantify the number of older adult facilities in Los Angeles County that are exposed to high levels of traffic derived air pollution, and propose policy solutions to reduce pollution exposure to this vulnerable subgroup. Distances between 20,362 intersections and 858 elder care facilities were estimated, and roads or highways within 500 of facilities were used to estimate traffic volume exposure. Of the 858 facilities, 54 were located near at least one major roadway, defined as a traffic volume over 100,000 cars/day. These 54 facilities house approximately 6,000 older adults. Following standards established for schools, we recommend legislation mandating the placement of new elder care facilities a minimum of 500 feet from major roadways in order to reduce unnecessary mortality risk from pollution exposure.

Association of Roadway Proximity with Fasting Plasma Glucose and Metabolic Risk Factors for Cardiovascular Disease in a Cross-Sectional Study of Cardiac Catheterization Patients

BACKGROUND

The relationship between traffic-related air pollution (TRAP) and risk factors for cardiovascular disease needs to be better understood in order to address the adverse impact of air pollution on human health.

OBJECTIVE

We examined associations between roadway proximity and traffic exposure zones, as markers of TRAP exposure, and metabolic biomarkers for cardiovascular disease risk in a cohort of patients undergoing cardiac catheterization.

METHODS

We performed a cross-sectional study of 2,124 individuals residing in North Carolina (USA). Roadway proximity was assessed via distance to primary and secondary roadways, and we used residence in traffic exposure zones (TEZs) as a proxy for TRAP. Two categories of metabolic outcomes were studied: measures associated with glucose control, and measures associated with lipid metabolism. Statistical models were adjusted for race, sex, smoking, body mass index, and socioeconomic status (SES).

RESULTS

An interquartile-range (990 m) decrease in distance to roadways was associated with higher fasting plasma glucose (β = 2.17 mg/dL; 95% CI: -0.24, 4.59), and the association appeared to be limited to women (β = 5.16 mg/dL; 95% CI: 1.48, 8.84 compared with β = 0.14 mg/dL; 95% CI: -3.04, 3.33 in men). Residence in TEZ 5 (high-speed traffic) and TEZ 6 (stop-and-go traffic), the two traffic zones assumed to have the highest levels of TRAP, was positively associated with high-density lipoprotein cholesterol (HDL-C; β = 8.36; 95% CI: -0.15, 16.9 and β = 5.98; 95% CI: -3.96, 15.9, for TEZ 5 and 6, respectively).

CONCLUSION

Proxy measures of TRAP exposure were associated with intermediate metabolic traits associated with cardiovascular disease, including fasting plasma glucose and possibly HDL-C.

Medication type modifies inflammatory response to traffic exposure in a population with type 2 diabetes

The association between residential traffic exposure and change in C-reactive protein over 2-years was evaluated using multivariate linear regression including interaction models for traffic and diabetes medication use/type (insulin vs. oral hypoglycemic agents (OHAs)). The study population was Puerto Rican adults (n = 356) residing in greater Boston with type 2 diabetes. Traffic was characterized as proximity to roads with >20,000 weekday traffic volumes, and multi-directional traffic density. Increases in CRP concentration were significantly associated with residence ≤100 m of a roadway (p = 0.009) or near multiple roadways (p < 0.001), vs. further away, for individuals using insulin in stratified models, with consistent results in interaction models (p = 0.071 and p = 0.002). CRP was significantly lower with highest traffic density exposure in stratified (p = 0.03) and interaction models (p = 0.024) for individuals using OHAs. Individuals on insulin experienced increased CRP concentrations with traffic exposure over a 2-year study period, while those using OHAs did not experience increases.

The association between air pollutants and morbidity for diabetes and liver diseases modified by sexes, ages, and seasons in Tianjin, China

With the generalized linear model and natural splines (ns), we examined the association between outdoor air pollutants and daily morbidity for diabetes and liver disease stratified by sexes and ages based on 4 years of daily data (2008-2011) in Tianjin, China. Season effects of air pollutants including particulate matter (PM), sulfur dioxide (SO2), and nitrogen dioxide (NO2) were also investigated. An increase of 10 μg/m(3) in a 2-day average concentrations of particulate matter with diameters of 10 μm or less (PM10), SO2, and NO2 corresponds to increases in diabetes morbidity of 0.39 % (95 % confidence interval (CI), -0.42-1.12), 0.15 % (95 % CI, -0.25-0.54), and 1.22 % (95 % CI, 0.51-2.96), respectively. As for liver morbidity, the increases were -0.84 % (95 % CI, -2.33-0.62), 0.90 % (95 % CI, 0.50-1.74), and 1.10 % (95 % CI, -2.58-4.78), respectively. The effects were stronger in the cool season than those in the warm season; females and the elderly were generally more vulnerable to outdoor air pollution. This study possesses scientific implications and instructional significance for local environmental standards and medical policymaking.

Main air pollutants and diabetes-associated mortality: a systematic review and meta-analysis

OBJECTIVE

Exposure to high levels of air pollutants may be linked to diabetes-associated mortality, but the associations remain unclear. To assess the associations between main air pollutants and diabetes-associated mortality, a systematic review and meta-analysis was performed.

METHODS

PubMed, Embase and Web of Science were searched for studies investigating the associations between increments in gaseous (nitrogen dioxide (NO2), sulphur dioxide, ozone (O3) and carbon monoxide) and particulate matter (PM; diameter<2.5 μm (PM2.5) or <10 μm (PM10)) air pollutants and diabetes-associated mortality. Using a random-effects model, relative risks (RRs) and 95% CIs were calculated per interquartile range (IQR) increment or per 10 μg/m3 increment in pollutant concentrations.

RESULTS

Out of 925 identified articles, 36 were reviewed in depth and 12 studies from 13 articles satisfying the inclusion criteria (five time-series, five case-crossovers and two cohorts) were finally included. Increased risk of diabetes-associated mortality was associated with higher levels of PM2.5 (per 10 μg/m3: RR=1.123, 95% CI 1.036-1.217, P=0.005, I2=96.1%), PM10 (per 10 μg/m3: RR=1.008, 95% CI 1.004-1.013, P<0.001, I2=0%), NO2 (per 10 μg/m3: RR=1.024, 95% CI 1.007-1.041, P=0.006, I2=49.7%) and O3 (per IQR increment: RR=1.065, 95% CI 1.017-1.115, P=0.007, I2=0.0%). No obvious risk of publication bias was observed.

CONCLUSIONS

Exposure to high levels of air pollutants is significantly associated with an increased risk of diabetes-associated mortality.

Residential traffic and incidence of Type 2 diabetes: the German Health Interview and Examination Surveys

AIMS

To investigate whether an indicator of overall traffic intensity is related to the risk of Type 2 diabetes in a nationwide cohort.

METHODS

The study population comprised 3604 adults aged 18-79 years and without diabetes from the German National Health Interview and Examination Survey (GNHIES98, 1997-1999) who participated again in a follow-up survey (DEGS1, 2008-2011). The association between the participants' reported traffic intensity at their residential address and Type 2 diabetes incidence was examined using logistic regression models.

RESULTS

During a mean of 12.1 years of follow-up, 252 of the participants included in the study developed Type 2 diabetes. Compared with people living in traffic-calmed areas, odds ratios were 1.15 (95% CI 0.80-1.67) for people living on moderately busy side streets, 1.11 (95% CI 0.69-1.80) for people living on considerably busy side streets, 1.41 (95% CI 0.96-2.08) for people living on heavily busy roads, and 1.97 (95% CI 1.07-3.64) for people living on extremely busy roads, after adjusting for age, sex, active and passive smoking, type of heating, education, BMI, waist circumference, sport activity and parental diabetes history.

CONCLUSIONS

The twofold higher risk of Type 2 diabetes observed for people exposed to intense traffic in this nationwide cohort extends the limited evidence from previous selected populations. Although the underlying traffic-related components and their biological mechanisms still need to be unravelled, traffic exposure control should be considered in public health strategies to reduce the global burden of diabetes.