Association Between Ambient Air Pollution and Diabetes
Association Between Ambient Air Pollution and Diabetes
In this systematic review, we considered 13 studies on different types of diabetes. The identified epidemiological evidence is highly diverse: Levels, timing, and assessment of exposure varied, as did the outcome definitions, measures of association, and degree of confounder control. The studies included persons with different age ranges and settings, and some populations included only women. Although there is a risk of bias, the results of the meta-analyses indicate a positive association between traffic-related air pollution and T2DM.
There is strong evidence supporting the role of inflammation in T2DM (Donath and Shoelson 2011; Sjöholm and Nyström 2006). Chronic activation of inflammatory mechanisms can contribute to chronic insulin resistance and subsequent T2DM. Air pollution has been shown to be inflammatory (Liu et al. 2013; Rajagopalan and Brook 2012). Its potential mechanisms in mediating T2DM include pulmonary and systemic inflammation, directly releasing cytokines, alterations in glucose homeostasis through defective insulin signaling in tissues, immune cells activation in visceral adipose tissues potentiating inflammation (Sun et al. 2009; Xu et al. 2010; Yan et al. 2011), and endoplasmic reticulum stress in the lung and liver in relation with hepatocyte and alveolar cells (Liu et al. 2013; Rajagopalan and Brook 2012). PM2.5 also acts as a hypothalamic stressor, inducing peripheral inflammation and abnormalities in glucose metabolism (Liu et al. 2013; Purkayastha et al. 2011). PM2.5 was also shown to mediate dysfunctional brown adipose and mitochondrial tissues (Liu et al. 2013; Rajagopalan and Brook 2012), which is one of the systemic pathologies in T2DM (Lowell and Shulman 2005).
Chuang et al. (2010) demonstrated that exposure to air pollution [PM ≤ 10 μm (PM10) and ozone] exposure leads to alteration in blood pressure, blood lipids, and hemoglobin A1c, a marker of blood glucose control. Kelishadi et al. (2009) found positive associations between exposure to PM10, NO2, and insulin resistance among children in Iran. Thiering et al. (2013) later found a positive association between residential proximity to traffic, particulate matter (PM10), NO2, and risk of insulin resistance [homeostatic model assessment (HOMA-IR)] among children who were part of a birth cohort in Germany. Exposure to traffic-related air pollution is also associated with impaired glucose tolerance in pregnancy (Fleisch et al. 2014). Experimental evidence also exists for the association of air pollution and type 1 diabetes (T1DM). Ozone is known to alter T-cell dependent immune response, predisposing to autoimmune diseases (Krishna et al. 1998). It may also damage the beta cells of the pancreas possibly as a result of pulmonary reactive oxidative species production and oxidative stress, leading to reduced insulin secretion (Brenner et al. 1993; Kelishadi et al. 2009). Together with sulfate, ozone may have apoptotic properties on the beta cells (Hathout et al. 2006). The use of antioxidant prophylaxis for T1DM also points to the possibility of oxidative or inflammatory mechanisms in T1DM (Albright and Goldstein 1996).
Although we applied a very broad search strategy and accepted any study design, there are few published studies on the association of air pollution with T1DM or GDM. In addition, some studies did not allow distinguishing adult T1DM from T2DM. Only three of the seven synthesized studies explicitly analyzed the T2DM risk (Coogan et al. 2012; Dijkema et al. 2011; Krämer et al. 2010). However, because > 90% of adult diabetes diagnoses are T2DM, this is unlikely to substantially affect the conclusions. Overall, the available data are not sufficient to evaluate associations with these diabetes types.
Our analysis on the association with T2DM was based on results from primary studies with unclear to high risk of bias and high diversity among the included studies. We took this into account by using effect estimates modeled to participants' residences, converting all effect estimates to a comparable unit (per 10 μg/m of exposure), stratifying analyses by sex, including only longitudinal studies, and performing other sensitivity analyses.
The high diversity among the studies was reflected in our observation of substantial heterogeneity in the meta-analysis for NO2 (Table 4), which synthesized longitudinal and cross-sectional data. This was not observed for PM2.5, for which all studies were longitudinal. However, the number of studies was too small to further analyze this heterogeneity.
Future studies should report scales of exposure assessment (pollutant quantification and traffic exposure proxies) that allow direct comparisons with existing evidence. It would be important to apply comparable models in assigning exposure to participants. Ideally, traffic distance measures should be replaced by objective particle concentration measures and models of near-road traffic-related pollutants such as ultrafine particles of elemental carbon. Also, it would be important to consider various time lags for exposure.
The studies on T1DM found associations with ozone and sulfates. These pollutants can be included in the future models for T2DM, because pollutants usually occur together in different proportions. Carbon monoxide, lead, oxidative metals, volatile organic compounds, and polycyclic aromatic hydrocarbons are other traffic-related pollutants that may be more deleterious to health but have been given less consideration.
Adjusting for noise exposure is also essential because air pollution and noise can be correlated (Foraster 2013; Kim et al. 2012; Ross et al. 2011; Tétreault et al. 2013) and share health effects. Sørensen et al. (2013) recently reported a positive association between road-traffic noise and incident diabetes, and another large meta-analysis of 10 epidemiologic studies by Cappuccio et al. (2010) found that both quality and quantity of sleep, which are related to noise, were significant predictors of the risk of T2DM. Consideration of noise is thus necessary in assessing the health effects of air pollution.
Also, socioeconomic variables should be adjusted on the spatial scale, apart from individual-level adjustment. Consideration for this spatial confounding is necessary when individual differences in health outcome are associated with neighborhood characteristics such as neighborhood socioeconomic status (Sheppard et al. 2012). It is crucial that studies on diabetes risk consider established diabetes risk factors including obesity, physical activity, and nutrition. Active and passive smoking should be considered when assessing the effect of air pollution. Lack of information on these creates a high risk for bias.
Other forms of bias such as the healthy survivor effect should be taken into account, especially in longitudinal studies. Raaschou-Nielsen et al. (2013b) demonstrated associations between diabetes mortality and NOx exposure; thus, diabetes patients exposed to air pollution could die and no longer participate, resulting in incorrect estimates of association if mortality was not taken into consideration.
No included study on this topic was done in developing countries. For generalizability of evidence, research should be extended to developing countries where air pollution (including indoor) is high. This could also help in understanding effects of different air pollution compositions. Indoor air pollution is also associated with diabetes as well as cardiovascular diseases (Lee et al. 2012) and is highly prevalent in developing nations (Lim et al. 2012).
Considering the ambiguity in dose–response relationship in air pollution studies (Smith and Peel 2010), future studies should assess air pollution diabetes association in a dose–response manner. This will help in identifying the point in the dose spectrum where control will yield the most benefits for health policy (Smith and Peel 2010).
Overall, the existing evidence indicates a positive association of air pollution and T2DM risk, although there is high risk of bias. High-quality longitudinal studies are needed (taking into consideration sources and composition of air pollution as well as biomarkers) to improve our understanding of this association.
Discussion
In this systematic review, we considered 13 studies on different types of diabetes. The identified epidemiological evidence is highly diverse: Levels, timing, and assessment of exposure varied, as did the outcome definitions, measures of association, and degree of confounder control. The studies included persons with different age ranges and settings, and some populations included only women. Although there is a risk of bias, the results of the meta-analyses indicate a positive association between traffic-related air pollution and T2DM.
Pathophysiologic Mechanisms of DM–Air Pollution Association
There is strong evidence supporting the role of inflammation in T2DM (Donath and Shoelson 2011; Sjöholm and Nyström 2006). Chronic activation of inflammatory mechanisms can contribute to chronic insulin resistance and subsequent T2DM. Air pollution has been shown to be inflammatory (Liu et al. 2013; Rajagopalan and Brook 2012). Its potential mechanisms in mediating T2DM include pulmonary and systemic inflammation, directly releasing cytokines, alterations in glucose homeostasis through defective insulin signaling in tissues, immune cells activation in visceral adipose tissues potentiating inflammation (Sun et al. 2009; Xu et al. 2010; Yan et al. 2011), and endoplasmic reticulum stress in the lung and liver in relation with hepatocyte and alveolar cells (Liu et al. 2013; Rajagopalan and Brook 2012). PM2.5 also acts as a hypothalamic stressor, inducing peripheral inflammation and abnormalities in glucose metabolism (Liu et al. 2013; Purkayastha et al. 2011). PM2.5 was also shown to mediate dysfunctional brown adipose and mitochondrial tissues (Liu et al. 2013; Rajagopalan and Brook 2012), which is one of the systemic pathologies in T2DM (Lowell and Shulman 2005).
Chuang et al. (2010) demonstrated that exposure to air pollution [PM ≤ 10 μm (PM10) and ozone] exposure leads to alteration in blood pressure, blood lipids, and hemoglobin A1c, a marker of blood glucose control. Kelishadi et al. (2009) found positive associations between exposure to PM10, NO2, and insulin resistance among children in Iran. Thiering et al. (2013) later found a positive association between residential proximity to traffic, particulate matter (PM10), NO2, and risk of insulin resistance [homeostatic model assessment (HOMA-IR)] among children who were part of a birth cohort in Germany. Exposure to traffic-related air pollution is also associated with impaired glucose tolerance in pregnancy (Fleisch et al. 2014). Experimental evidence also exists for the association of air pollution and type 1 diabetes (T1DM). Ozone is known to alter T-cell dependent immune response, predisposing to autoimmune diseases (Krishna et al. 1998). It may also damage the beta cells of the pancreas possibly as a result of pulmonary reactive oxidative species production and oxidative stress, leading to reduced insulin secretion (Brenner et al. 1993; Kelishadi et al. 2009). Together with sulfate, ozone may have apoptotic properties on the beta cells (Hathout et al. 2006). The use of antioxidant prophylaxis for T1DM also points to the possibility of oxidative or inflammatory mechanisms in T1DM (Albright and Goldstein 1996).
Strengths and Limitations
Although we applied a very broad search strategy and accepted any study design, there are few published studies on the association of air pollution with T1DM or GDM. In addition, some studies did not allow distinguishing adult T1DM from T2DM. Only three of the seven synthesized studies explicitly analyzed the T2DM risk (Coogan et al. 2012; Dijkema et al. 2011; Krämer et al. 2010). However, because > 90% of adult diabetes diagnoses are T2DM, this is unlikely to substantially affect the conclusions. Overall, the available data are not sufficient to evaluate associations with these diabetes types.
Our analysis on the association with T2DM was based on results from primary studies with unclear to high risk of bias and high diversity among the included studies. We took this into account by using effect estimates modeled to participants' residences, converting all effect estimates to a comparable unit (per 10 μg/m of exposure), stratifying analyses by sex, including only longitudinal studies, and performing other sensitivity analyses.
The high diversity among the studies was reflected in our observation of substantial heterogeneity in the meta-analysis for NO2 (Table 4), which synthesized longitudinal and cross-sectional data. This was not observed for PM2.5, for which all studies were longitudinal. However, the number of studies was too small to further analyze this heterogeneity.
Prospects
Future studies should report scales of exposure assessment (pollutant quantification and traffic exposure proxies) that allow direct comparisons with existing evidence. It would be important to apply comparable models in assigning exposure to participants. Ideally, traffic distance measures should be replaced by objective particle concentration measures and models of near-road traffic-related pollutants such as ultrafine particles of elemental carbon. Also, it would be important to consider various time lags for exposure.
The studies on T1DM found associations with ozone and sulfates. These pollutants can be included in the future models for T2DM, because pollutants usually occur together in different proportions. Carbon monoxide, lead, oxidative metals, volatile organic compounds, and polycyclic aromatic hydrocarbons are other traffic-related pollutants that may be more deleterious to health but have been given less consideration.
Adjusting for noise exposure is also essential because air pollution and noise can be correlated (Foraster 2013; Kim et al. 2012; Ross et al. 2011; Tétreault et al. 2013) and share health effects. Sørensen et al. (2013) recently reported a positive association between road-traffic noise and incident diabetes, and another large meta-analysis of 10 epidemiologic studies by Cappuccio et al. (2010) found that both quality and quantity of sleep, which are related to noise, were significant predictors of the risk of T2DM. Consideration of noise is thus necessary in assessing the health effects of air pollution.
Also, socioeconomic variables should be adjusted on the spatial scale, apart from individual-level adjustment. Consideration for this spatial confounding is necessary when individual differences in health outcome are associated with neighborhood characteristics such as neighborhood socioeconomic status (Sheppard et al. 2012). It is crucial that studies on diabetes risk consider established diabetes risk factors including obesity, physical activity, and nutrition. Active and passive smoking should be considered when assessing the effect of air pollution. Lack of information on these creates a high risk for bias.
Other forms of bias such as the healthy survivor effect should be taken into account, especially in longitudinal studies. Raaschou-Nielsen et al. (2013b) demonstrated associations between diabetes mortality and NOx exposure; thus, diabetes patients exposed to air pollution could die and no longer participate, resulting in incorrect estimates of association if mortality was not taken into consideration.
No included study on this topic was done in developing countries. For generalizability of evidence, research should be extended to developing countries where air pollution (including indoor) is high. This could also help in understanding effects of different air pollution compositions. Indoor air pollution is also associated with diabetes as well as cardiovascular diseases (Lee et al. 2012) and is highly prevalent in developing nations (Lim et al. 2012).
Considering the ambiguity in dose–response relationship in air pollution studies (Smith and Peel 2010), future studies should assess air pollution diabetes association in a dose–response manner. This will help in identifying the point in the dose spectrum where control will yield the most benefits for health policy (Smith and Peel 2010).
Overall, the existing evidence indicates a positive association of air pollution and T2DM risk, although there is high risk of bias. High-quality longitudinal studies are needed (taking into consideration sources and composition of air pollution as well as biomarkers) to improve our understanding of this association.
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