diabetes research and clinical practice 106 (2014) 161–172

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Diabetes Research and Clinical Practice journ al h ome pa ge : www .elsevier.co m/lo cate/diabres

Air pollution and risk of type 2 diabetes mellitus: A systematic review and meta-analysis Eric V. Balti a, Justin B. Echouffo-Tcheugui b,c, Yandiswa Y. Yako d,e, Andre P. Kengne d,f,g,* a

Diabetes Research Center, Faculty of Medicine and Pharmacy, Brussels Free University, Brussels, Belgium Hubert Department of Global Health, Rollins School of Public Health, Emory University; Atlanta, GA, USA c MedStar Hospital System, Baltimore, MD, USA d Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa e Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa f Department of Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa g The George Institute for Global Health, The University of Sydney, Sydney, Australia b

article info

abstract

Article history:

Aim: Whether exposure to relatively high levels of air pollution is associated with diabetes

Received 9 June 2014

occurrence remains unclear. We sought to assess and quantify the association between

Received in revised form

exposure to major air pollutants and risk of type 2 diabetes.

27 July 2014

Methods: PubMed and EMBASE databases (through September 2013) were searched using a

Accepted 22 August 2014

combination of terms related to exposure to gaseous (NO2 and NOx) or particulate matter

Available online 10 September 2014

pollutants (PM2.5, PM10 and PM10–2.5) and type 2 diabetes. Descriptive and quantitative information were extracted from selected studies. We used random-effects models

Keywords:

meta-analysis to derive overall risk estimates per type of pollutant.

Air pollution

Results: We included ten studies (five cross-sectional and five prospective), assessing the

Particulate matters pollutants

effects of air pollutants on the occurrence of diabetes. In prospective investigations, the overall

Gaseous pollutants Type 2 diabetes Insulin resistance Inflammation

effect on diabetes occurrence was significant for both NO2 (adjusted hazard ratio [HR], 1.13; 95% confidence interval [95%CI], 1.01–1.22; p < 0.001; I2 = 36.4%, pheterogeneity = 0.208) and PM2.5 (HR, 1.11; 95%CI, 1.03–1.20; p < 0.001; I2 = 0.0%, pheterogeneity = 0.827). Odds ratios were reported by two cross-sectional studies which revealed similar associations between both NO2 and PM2.5 with type 2 diabetes. Across studies, risk estimates were generally adjusted for age, gender, body mass index and cigarette smoking. Conclusions: Available evidence supports a prospective association of main air pollutants with an increased risk for type 2 diabetes. This finding may have implications for population-based strategies to reduce diabetes risk. # 2014 Elsevier Ireland Ltd. All rights reserved.

* Corresponding author at: South African Medical Research Council, PO Box 19070, Tygerberg, 7505 Cape Town, South Africa. Tel.: +27 21 938 0529; fax: +27 21 938 0460. E-mail addresses: [email protected], [email protected], [email protected] (A.P. Kengne). http://dx.doi.org/10.1016/j.diabres.2014.08.010 0168-8227/# 2014 Elsevier Ireland Ltd. All rights reserved.

162

1.

diabetes research and clinical practice 106 (2014) 161–172

Introduction

The interest for the epidemiology of air pollution and chronic diseases is rapidly increasing. In fact, air pollution has been intensively investigated in relation with cardiopulmonary diseases or mortality [1–4]. Diabetes mellitus on the other hand has received less attention. Air-pollution related diabetes may represent a serious and increasing threat to the health of populations. Indeed, there is growing evidence suggesting a connection between particulate air pollution and T2DM occurrence and progression [5,6]. Regulated pollutants thought to have an effect on the risk of T2DM include among others, particulate matter pollutants of 2.5 mm in aerodynamic diameter (PM2.5) or less, which are derived from fossil-fuel combustion by motor vehicles and stationary sources such as power plants [7,8]. These micro particles are potential health hazardous molecules because of their small size, that confers them the capacity to enter the airways, leading to their deposition on the pulmonary alveolocapillary membrane and therefore increasing their likelihood to trigger a systemic inflammatory response [9]. The magnitude of the association between air pollutants and T2DM, if any, largely remains unclear, with some studies suggesting a positive association [10–14], and other failing to establish a link [15]. To our knowledge, there is currently no available comprehensive analysis of available studies of pollutants and T2DM using a systematic approach and accounting for their specificities. We therefore conducted a systematic review to clarify the extent of a potential association between exposure to air pollution and the occurrence of T2DM, and to identify the gaps in the existing evidence. We hypothesized that exposure to air-borne particles is significantly associated with an increased risk of T2DM.

2.

Methods

2.1.

Search strategy and study selection

We searched PubMed and EMBASE for studies published from inception until September 15, 2013, using a combination of search terms related to diabetes (‘‘diabetes mellitus’’, ‘‘type 2 diabetes’’ and ‘‘hyperglycemia’’) and to air pollution (‘‘air pollution’’, ‘‘ultra-fine particles’’, ‘‘traffic-related pollutants’’, ‘‘particulate matters’’, ‘‘nitrogen dioxide’’, ‘‘carbon monoxide’’ and ‘‘nitrogen dioxide’’). We supplemented the electronic searches by scanning the reference lists of relevant publications, and identifying their citations through the ISI Web of Science. For articles without an abstract or enough information in the abstract to make a decision, the full text, and where necessary supplementary materials, were reviewed before a decision was made. Cross-sectional, case-control and cohort studies reporting a quantitative measure (odds ratio, relative risk, hazard ratio or accretion in diabetes frequency) of the association between exposure to air pollution and risk of T2DM were eligible. Air pollutants were restricted to exclusive air pollutants as listed by the World Health Organization (WHO) and US Environmental Protection Agency (EPA) [15,16]. We therefore focused

on the following major air pollutants: gaseous pollutants (O3, CO, SO2, NO2 and NOx) and particulate matter pollutants (PM2.5, PM10 and PM10–2.5). Only studies involving humans were included and no language restriction was applied. Studies conducted among individuals with type 1 diabetes only or gestational diabetes, or reporting the association between T2DM and persistent organic pollutants (POPs) were excluded.

2.2.

Data extraction and validity assessment

Two reviewers (EVB and YYY) independently selected studies and conducted data extraction. Disagreements were adjudicated by a third investigator (JBE). We contacted authors for additional data or clarification where necessary. We extracted data on study settings, design, population characteristics, type of pollutant, measurement of exposure to pollutants, diagnosis and definition of diabetes, duration of follow-up (for cohort studies) and study period.

2.3.

Data synthesis

We summarized extracted data in tables and performed a narrative synthesis of all the included studies. Effect estimates for the exposure to NOx were pooled after conversion into corresponding NO2 values using the conversion factor of 0.75 [17]. One study reported NO2 concentration in parts per billion [11]. This value was converted into micrograms per cubic metre using the conversion factor 1.88. PM10 effect estimates were converted to PM2.5 values using the conversion factor 0.7 [18]. All effect estimates reported per interquartile range were transformed into effects per 10 mg/m3 for each pollutant prior to the meta-analysis [11]. We used random-effects model meta-analysis to assess the pooled risk estimates reported as hazard ratio [HR] or odds ratios and 95% confidence intervals [CIs] independently of the study design. Heterogeneity across studies was assessed statistically using the I-squared (I2) test; and was distinguished as low (I2  25%), moderate (25% < I2 50%) or high (I2  75%) [19]. Two studies reported measures of association using single and multipollutant models [20,21]; thus we reported hazard ratios obtained from single pollutant models to ease comparison between studies. All analyses were done using Stata version 12 (StataCorp, TX, USA); tests were two-sided and a p-value 200 mg/dl) 2 hrs after OGTT; at least one diabetes-associated symptom with a single elevated plasma glucose concentration or treatment with OAM

diabetes research and clinical practice 106 (2014) 161–172

Author, published Study design year

Cross-sectional

Patients aged 40 years attending two respiratory clinics in Hamilton and Toronto/Canada

1991–1999

NO2

Land-use regressionderived estimates using GIS system (ArcGIS)

Djikema et al. [24]

Cross-sectional

1998–2000

NO2

Residential NO2 concentration (land-use regression model)

Lockwood [14]b

Cross-sectional

Residents of Westfriesland population aged 50–75 years/The Netherlands Randomly selected participants contacted by phone/ USA

2000

Total air pollutants

Eze et al. [25]

Cross-sectional

Switzerland

2002

Pearson et al. [13]b

Ecological

Hispanic, Asian, native American, African American and Caucasian inhabitants of 3082 US counties/USA

2003–2005

One 2-week period (in the year 2002) for Hamilton and average of two 2-week periods of measurement in fall 2002 and spring 2004 for Toronto One-year average (derived from an average of one-week measurement during each of the four seasons of 2007)

ICD9:250; diagnosis in two or more claims by GP; one claim by specialist or diagnosis made in any hospitalization

Toxics Release Inventory data www.epa.gov

One-year (total air release from all industries for the year 1999)

PM10 and NO2

Modeled average exposure at residential area

Exposure over the 10 preceding years

PM2.5

Annual mean PM2.5 levels by county derived by GIS (ArcGIS)

One-year (annual weighted mean per county)

Self-reported diabetes registered via phone calls (Behavioral Risk Factors Surveillance System data [BRFSS]) Self-reported diabetes, doctordiagnosed DM, anti-diabetic medication, RBG level >11.1 mmol/l or HbA1c > 6.5% (48 mmol/mol) Self-reported patients with diabetes aged >20 years registered in the CDC’s BRFSS via monthly telephone survey

Doctor-diagnosed T2DM, fasting capillary glucose or OGTT-based diagnosis of T2DM (WHO 2006 criteria)

DM: diabetes mellitus; NHS: nurses’ health study; HPFS: health professionals follow-up study; GIS: geographic information system; GP: general practitioner; ICD: international classification of diseases; CDC: center of disease control and prevention; WHO: world health organization; OAM: oral anti-diabetic medication; ppb: parts per billion; T2DM: type 2 diabetes mellitus, FBG: fasting blood glucose; RBG: random blood glucose; OGTT: oral glucose tolerance test; NASA: national aeronautic and space administration; SALIA: Study on the Influence of Air Pollution on Lung, Inflammation and Aging. a Follow-up period only applicable for cohort studies. b Type of diabetes not specified. c Inclusion criteria to the Danish Diabetes Register. d All cases are assumed to be type 2 diabetes.

diabetes research and clinical practice 106 (2014) 161–172

Brook et al. [11]b

165

166

Table 2 – Summary of exposure and outcome assessment strategies; and estimate of effect size in included studies. Number of events

Effect estimate

Comparison group/variable

Exposure Group/subgroups

Effect size 95%CI

Adjustment factors

Andersen et al. [10]

51,818

2877a

HR

IQR (4.9 mg/m3)

NO2

All participants Traffic road within 50 m Traffic road within 100 m

1.04 1.06 1.02

1.00–1.08 0.94–1.20 1.00–1.04

Chen et al. [23]

60,076b

6310b

HR

10 mg/m3 PM2.5 accretion

PM2.5

Men (n = 622/3452) Women (n = 630/4182) All participants BMI 30 kg/m2 (n = 2415) Men (n = 3239) Women (n = 3071)

0.99 1.04 1.11 1.20 1.08 1.08 1.03 1.17

0.95–1.03 1.00–1.08 1.02–1.21 1.00–1.45 0.94–1.25 0.94–1.25 0.91–1.16 1.03–1.32

Coogan et al. [20]

3992

183

HRc

10 mg/m3 PM2.5 accretion IQR (12.3 mg/m3)

PM2.5 NOx

All participants All participants

1.63 1.25

0.78–3.44 1.07–1.46

Kramer et al. [12]

1775

187

HR

IQR (10.1 mg/m3)

PM2.5

Monitoring stations

1.16

0.81–1.65

Sex, age, BMI, waist-tohip ratio, smoking status, smoking duration, smoking intensity, environmental tobacco smoke, educational level, physical activity in leisure time, alcohol consumption, fruit consumption, fat consumption, calendar year, self-reported hypertension, hypercholesterolemia and previous myocardial infarction Sex, marital status, education, household income, BMI, physical activity, smoking, alcohol consumption, diet, hypertension, race, urban residency, neighborhood-level unemployment rate, neighborhood-level education, and neighborhood-level household income and stratified by age, survey year and region Age, BMI, years of education, number of people in the household, smoking, drinks per week, hours per week of vigorous physical activity Age, BMI, heating with fossil fuels, workplace exposure with dust/ fumes, smoking, extreme temperatures, education

diabetes research and clinical practice 106 (2014) 161–172

Author, published Sample year size

Kramer et al. [12]

1775

187

HR

IQR IQR IQR IQR

(0.87 mg/m3) (24.9 mg/m3) (19.0 mg/m3) (15.0 mg/m3)

PM2.5 NO2 NO2 NO2

Age, BMI, heating with fossil fuels, workplace exposure with dust/ fumes, extreme temperatures, smoking, education

1.03 1.04 1.04

0.96–1.10 0.99–1.09 0.99–1.09

Age, season, calendar year, state of residence, timevarying cigarette smoking (status and pack-years), timevarying hypertension, baseline BMI, timevarying alcohol intake, baseline physical activity, and timevarying diet Age, season, calendar year, state of residence, timevarying cigarette smoking (status and pack-years), timevarying hypertension, baseline BMI, timevarying alcohol intake, baseline physical activity, and timevarying diet Age, season, calendar year, state of residence, timevarying cigarette smoking (status and pack-years), timevarying hypertension, baseline BMI, timevarying alcohol intake, baseline physical activity, and timevarying diet Age, BMI, and neighborhood income

1.15 1.34 1.15 1.42 3.51 0.85 0.90 1.02

NHS and HPFS cohorts 89, 460

4472

HRc

IQR (4 mg/m3) increase IQR (7 mg/m3) increase IQR (4 mg/m3) increase

PM2.5 PM10 PM10–2.5

Puett et al. [21]

NHS cohort 74, 412

3784

HRc

IQR (4 mg/m3) increase IQR (7 mg/m3) increase IQR (4 mg/m3) increase

PM2.5 PM10 PM10–2.5

Women Women Women

1.02 1.03 1.04

0.94–1.09 0.98–1.09 0.98–1.10

Puett et al. [21]

HPFS cohort 15, 048

688

HRc

IQR (4 mg/m3) increase IQR (7 mg/m3) increase IQR (4 mg/m3) increase

PM2.5 PM10 PM10–2.5

Men Men Men Distance to road 0–49 m 50–99 m 100–199 m 200 m

1.07 1.06 1.04 1.11 0.96 0.96 1.00

0.92–1.24 0.94–1.20 0.93–1.16 1.01–1.23 0.63–1.48 0.87–1.06 Reference

Brook et al. [11]

7634

1252

OR

1 parts per billion (ppb) accretion

NO2

All participants Men (n = 622/3452) Women (n = 630/4182)

1.01 0.99 1.04

0.98–1.05 0.95–1.03 1.00–1.08

167

Puett et al. [21]

diabetes research and clinical practice 106 (2014) 161–172

1.04–1.27 1.02–1.76 1.04–1.27 1.16–1.73 1.50–8.23 0.42–1.70 0.78–1.12 0.77–1.34

Emission inventory (traffic) Monitoring stations Emission inventory (traffic) Land-use regression Within 100 m from busy road and low educational status Within 100 m from busy road and high educational status Census 2000g (12 km) Census 2000g (Ground level) All participants (men and women) All participants (men and women) All participants (men and women)

168

Table 2 (Continued ) Number of events

Effect estimate

Comparison group/variable

Djikema et al. [24]

8018

619

OR

1st quartile

Eze et al. [25]

6, 392

315

OR

10 mg/m3 PM10 or NO2 accretion

Lockwood [14]

184,450

13,465 (7.3%)

Pearson et al. [13]

3082 US counties

Not provided

correlation coefficient % increase of diabetes per 10 mg/m3 of PM2.5

d,e

Prevalence of diabetes 10 mg/m3 PM2.5 accretion

Exposure Group/subgroups

Effect size 95%CI

NO2

2nd quartile 3rd quartile 4th quartile Distance to the nearest road 2nd quartile 3rd quartile 4th quartile Adjusted for age and gender Adjusted for all factors

1.03 1.25 0.80 1.12 1.17 0.88

0.82–1.31 0.99–1.56 0.63–1.02 0.88–1.42 0.93–1.48 0.70–1.13

Average monthly income, age and gender

1.43 1.41

1.18–1.74 1.17–1.69f

NO2

Adjusted for age and gender Adjusted for all factors

1.23 1.21

1.06–1.43 1.04–1.40f

Total air pollutants PM2.5

All participants

0.54

( p < 0.0001)

Age, gender, educational level, neighborhood socioeconomic index, lifestyle characteristics, BMI, noise, hypertension, high-hsCRP and dyslipidemia Age, gender, educational level, neighborhood socioeconomic index, lifestyle characteristics, BMI, noise, hypertension, high-hsCRP and dyslipidemia Not provided

Census 2000g (36 km)

0.92

0.75–1.13

PM10

Adjustment factors

Age, gender, per capita income, educational level according to age (>25 and 25 years), race, health insurance, obesity, physical activity, latitude, and population density

HR: hazard ratio; OR: odd ratio; BMI: body mass index; COPD: chronic obstructive pulmonary disease; NHS: nurse’s health study; HPFS: health professionals follow-up study, high-hsCRP: high hypersensitive C-reactive protein defined as CRP level > sample median (1.0 mmol/l). a Confirmed cases. b Study participants with complete data. c Pollutants modeled separately. d Lowest level of exposure. e No association found in the case of traffic at nearest main road or traffic in 250 m buffer in comparison the first quartile of the distance. f Analyses realized in n = 6 111. g Census 2000 based on 2005 data.

diabetes research and clinical practice 106 (2014) 161–172

Author, published Sample year size

169

diabetes research and clinical practice 106 (2014) 161–172

Fig. 2 – Forest plot showing of the association between gaseous air pollutants [NO2 and NOx] and diabetes mellitus. Monitoring stations.

Among cross-sectional studies, one reported the percentage of accretion in diabetes prevalence associated with a 10 mm increase in PM2.5 which was 0.92% (95%CI, 0.75–1.13) [13] after adjustment for age, gender, per capita income, educational level according to age, race, health insurance, obesity, physical activity, latitude, and population density [13]. A second cross-sectional study correlated the prevalence of diabetes vs. total air release for all industries (Toxic release inventory [TRI]) across states in the USA, and found a significant relationship between TRI air release and diabetes prevalence (correlation coefficient r = 0.54, p < 0.0001) [14]. Of the three remaining cross-sectional studies, one found a significant association in women only for each one part per billion higher NO2 in relation with diabetes risk (OR, 1.04; 95%CI, 1.00–1.08) [11], the second described a positive association for both NO2 (OR, 1.19; 95%CI, 1.03–1.38) and PM10 (OR, 1.40; 95%CI, 1.17–1.67) with diabetes in a Swiss cohort [25]. In the third report, the authors found no association between NO2 and risk of diabetes when comparing participants in the upper vs. those in the lowest quarters of NO2 exposure [24].

a

Four studies investigated the association of proximity to main roads and traffic density with diabetes risk [10,12,21,24]. Some of the studies suggested a positive association for distances less than 100 m to high density road traffic [12,21]. However, for the same distance to the main road, Kramer et al. found a significant association in women with low educational status but not in those with high educational level [12]. We pooled data from included prospective studies to assess the extent of the association of NO2 and PM2.5 with diabetes. Five studies assessed the association of gaseous pollutants with diabetes mellitus. After conversion to the estimate of NOx to its NO2 corresponding value, the pooled effect (hazard ratio [HR]) across cohort studies reporting association between 10 mg/m3 increase in NO2 and diabetes risk was 1.13 (95%CI 1.04–1.22; p < 0.001; I2 = 36.4%, pheterogeneity = 0.208; Fig. 2). The pooled estimates from the two cross-sectional studies reporting association of NO2 with T2D suggested a positive relationship (OR, 1.16; 95%CI, 1.00–1.35; p < 0.001; I2 = 23.2%, pheterogeneity = 0.254; Fig. 2). Similarly, the corresponding figures were 1.11 (95%CI 1.03–1.20; p < 0.001; I2 = 0.0%, pheterogeneity = 0.827; Fig. 3) for 10 mg/m3 increase in PM2.5 after pooling five studies

Fig. 3 – Forest plot showing of the association between particulate matter pollutants [PM2.5 and PM10] and diabetes mellitus. a Monitoring stations.

170

diabetes research and clinical practice 106 (2014) 161–172

reporting the effect of PM2.5. Estimate associated to the exposure to PM10 reported in one of the five studies was converted to PM2.5 value (OR, 1.62; 95%CI, 1.25–2.09; Fig. 2).

4.

Discussion

The implementation of our search strategy yielded a limited number of studies reporting the association of air pollutants and type 2 diabetes. All reported studies were from developed countries (US, Canada and Europe). Nevertheless, currently available data suggest a modest but significant association between pollutants (NO2 and PM2.5) and diabetes occurrence which are associated to a 13% and 11% increase in diabetes risk, respectively for NO2 and PM2.5. There was minimal heterogeneity in both NO2 and PM2.5 analyses. Existing evidence from mechanistic studies suggest a number of possible biological pathways linking air pollutants to diabetes. One mechanism is endothelial dysfunction, which precedes insulin resistance. Indeed, particulate matter pollutants have been described as a mediator of endothelial dysfunction, implicated in reduced peripheral glucose uptake [26–29]. The second potential mechanism is a dysregulation of the visceral adipose tissue through inflammation [26,27], which is the pathophysiologic hallmark of T2DM. One of the studies included in this review reported that the association of inflammation (as assessed by blood levels of complement fragment C3c) with propensity to diabetes was enhanced by exposure to particulate matters [12], with women with elevated C3c plasma level having higher susceptibility to diabetes when exposed to particulate matters (HR, 1.09; 95%CI, 1.06–1.12; p < 0.001 for 1 inter-quartile range increase in PM10 level) [12]. This finding suggests a possible mediating role of low-grade inflammation on diabetes risk conferred by exposure to air pollutants. Hepatic insulin resistance is the third potential mechanism of diabetes pathogenesis due to pollutants. PM2.5 exposure decreases tyrosine phosphorylation in the liver but does not affect insulin receptor substrate 1 (IRS-1) levels [5,28]. IRS phosphorylation defects may induce an alteration of insulin stimulated glucose transporter translocation [5]. PM2.5 can induce endoplasmic reticulum stress and a significant increase in the unfolded protein response (UPR)-associated proteins in the lungs and liver, indicating activation of the ATF6 pathways; which is correlated with apoptosis in these organs. The UPR intersects with a variety of inflammatory and stress-signaling systems, all of which may influence lipid and glucose metabolism [28]. Furthermore, exposure to PM2.5 may induce a non-alcoholic steatohepatitis (NASH), with subsequent alteration of hepatic glycogen storage [29]. Mitochondrial dysfunction and brown adipose tissue alterations may also explain the effect of pollutants on the occurrence of diabetes. Exposure to PM2.5 induces decrease in brown adipose tissue and mitochondrial size; leading to a down regulation of insulin sensitivity [26,30]. An independent association between different markers of pollution, inflammation, oxidative stress and insulin resistance was shown in an Iranian community-based study [31]. Similarly, short term (5-day) exposure to low dose PM2.5 was shown to reduce metabolic insulin sensitivity in healthy individuals [32].

Other potential mechanisms leading to an increased cardiometabolic risk upon exposure to air pollutants include the effects of inflammation on the hypothalamic region via IL-6 and NF-kB leading to an impairment of glucose metabolism [5,33], as well as arterial vasoconstriction related to particulate pollutants and ozone, with a possible increase in cardiovascular risk [34,35]. Beside diabetes, environmental pollution has been shown to be associated with other cardiometabolic disorders including hypertension and obesity [36,37], as well as alterations in hemoglobin A1C, blood pressure, insulin resistance and lipid profile as shown in a Taiwanese population exposed to PM2.5, PM10 and NO2 [38–40]. Since the combination of these conditions has been used to define metabolic syndrome (MetS) [41], it is likely that exposure to air pollutants induces diabetes in the context of MetS [31,38]. One can speculate that diabetes susceptibility conferred by air-pollutants may vary with the pre-existing components of MetS [23,42]. Although this has been accounted for in most of the included studies through adjustments [10–13,20,21,23,24], more investigation is needed to determine the incremental risk associated with exposure to air pollutants in the presence of a specific co-morbid condition and to the number of components of MetS. Our findings are similar to those observed with other cardio-metabolic outcomes including myocardial infarction [43], heart failure [44], as well as hypertension and obesity [36,37] or mortality [4]. Included studies were from US, Canada and Europe and we did not retrieve any study from developing countries (India, China and South America). Therefore, data on the effect of air pollutants on the occurrence of diabetes in developing countries (which are home to the highest burden of diabetes [45]) is highly needed as concentrations of air pollutant in these countries are most probably above those observed in the US and Europe, partly because of the epidemiological transition with rapid urbanization with the consequential increased exposure to environmental pollutants [46]. The majority of studies used single-pollutant models, which do not take into account the potential interaction between pollutants. Traffic-related noise was explored only in few studies [10,21,24]. Although no consistent results were obtained, Kramer et al. [12] found a positive association between proximity with busy roads and risk of diabetes in individuals with low educational status. It is therefore likely that a model accounting for all possible sources of air pollution would be more accurate than single pollutant models. Also, exposure assessment strategies differed between studies and most of the estimates were based on outdoor or periresidential area levels of exposure. There is therefore no information about the effects of indoor air pollution on susceptibility to type 2 diabetes. In the regression analysis, different adjustment factors were used in the included studies; the factors commonly adjusted for were age, sex, body mass index and smoking. Furthermore, the long-term lag time structure of the examined pollutants was not accounted for in the examined studies. Currently described effects have focused on relatively short-time lagged effect estimates; although the effect of the latter could be important, longer distributed lag models may uncover higher effect size. The above limitation as well as the heterogeneity in assessment

diabetes research and clinical practice 106 (2014) 161–172

strategies used to examine exposures in the various reports could be sources of bias in included studies. Children and adolescents were not examined in the included studies; this subgroup is of importance given their potentially high level of exposure to air pollution, the rising burden of type 2 diabetes in these age groups [47]; and the growing evidence that exposure to environmental chemicals during early life can interfere with adipose tissue biology (modulation of gene expression, promotion of MetS and disruption of endocrine signaling pathways and homeostatic weight controls), thus increasing the risk of T2DM [48,49]. There was likely a misclassification of some cases of diabetes across studies since even the studies that clearly specified T2DM as study endpoint relied on self-report, and very few [21,24] relied on oral glucose tolerance test for diagnosing diabetes. However, such a misclassification probably biased our effect estimate toward the null hypothesis of no association. None of the studies assessed the effect of lifetime exposure to pollutants, thus not accounting the time-varying nature of the exposure; however, this may simply reflect the practical difficulty of conducting such an assessment in epidemiological studies. Finally, not all the included studies did assess a dose-response association of air pollutants with diabetes. The strengths of our study include comprehensive searches across multiple databases without language restriction and a robust meta-analysis after harmonizing the exposures (10 mg/m3 of NO2 or PM2.5). Some limitations need however to be mentioned. These include the limited number of included studies and the difference in exposure assessment strategies in the included studies. In brief, we have demonstrated a modest increase in risk of diabetes associated with exposure to major air pollutants; relatively low levels of pollution were related to diabetes occurrence supporting a strong plausibility of our results. Although the increase in the relative risk for diabetes due to air pollution for an individual would seem small compared with the effect of established diabetes risk factors, given the enormous number of people who are likely exposed to air pollution, even conservative risk estimates would still translate into a substantial increase in the population attributable fraction of diabetes related to air pollutants. Additional research is therefore needed to confirm the currently available evidence, and to explore the impact of air-borne pollutants in populous cities of developing countries. Further investigations on whether effective interventions that improve air quality are associated with a decrease incidence of type 2 diabetes are warranted.

Conflict of interest statement The authors have no conflict of interest to declare.

Contributor statement EVB: Performed literature search, extracted data, analysed data, drafted and edited the manuscript. YYY: Performed literature search, extracted data, drafted and edited the manuscript.

171

JBE: Performed literature search, extracted data, drafted and edited the manuscript. APK: Performed literature search, analysed data, drafted and edited the manuscript.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.diabres.2014.08.010.

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