Eur J Epidemiol (2014) 29:231–242 DOI 10.1007/s10654-014-9907-2

REVIEW

Systematic review and metaanalysis of air pollution exposure and risk of diabetes Mohsen Janghorbani • Fatemeh Momeni Marjan Mansourian

•

Received: 23 December 2013 / Accepted: 23 April 2014 / Published online: 4 May 2014 Ó Springer Science+Business Media Dordrecht 2014

Abstract The present systematic review and metaanalysis of published observational studies was conducted to assess the health effects of exposure to air pollution on diabetes risk. Online databases were searched through January 2013, and the reference lists of pertinent articles reporting observational studies in humans were examined. Pooled relative risks and 95 % confidence intervals were calculated with a random-effects model. Exposure to air pollution was associated with slight increase in risk of diabetes and susceptibility of people with diabetes to air pollution. These results were consistent between time-series, case-crossover and cohort studies and between studies conducted in North America and Europe. The association between exposure to air pollution and diabetes was stronger for gaseous pollutants than for particulate matter. Our metaanalysis suggests that exposure to air pollution may be a risk factor for diabetes and increase susceptibility of people with diabetes to air pollution. Keywords Air pollution
Diabetes mellitus
Metaanalysis

Introduction Diabetes mellitus is a major cause of morbidity and premature mortality worldwide, and its incidence and prevalence are increasing in both developed and developing nations [1]. This increase has been linked to modern lifestyle characteristics including obesity and physical inactivity [2]. M. Janghorbani (&)
F. Momeni
M. Mansourian Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran e-mail: [email protected]; [email protected]

However, exposure to air pollution has also been suggested as a contributing factor to this increasing incidence and prevalence [3–11]. Although several studies have investigated the association between exposure to air pollution and diabetes incidence, prevalence or mortality, the role of exposure to air pollution as a risk factor for diabetes remains unknown [3–5, 12–17]. Some studies reported a slight increase in the risk of diabetes or daily mortality among people with diabetes [4, 13–15, 18–20], whereas others reported no association [12, 17, 21–25] or an association only in men or women [3–5, 16, 26]. The results of other reports, however, were not statistically significant and hence inconclusive, making the results of these studies difficult to interpret. The inconsistencies noted above among different studies may be related to many factors, including chance, sample size, study design, risk factors, differing pollutants, variation across the populations, differing analytic methods, and issues related to data quality, measurement error and other characteristics. In addition, inconsistencies may also be related to differences in the outcomes studied, the incidence and prevalence of diabetes, hospitalizations, diabetesrelated mortality and the short-term or long-term timeframe of different studies of exposure to air pollution. We conducted a systematic review and metaanalysis of cross-sectional, time-series, case–crossover and cohort studies to summarize the epidemiologic evidence of the risk of diabetes as well as susceptibility of people with diabetes associated with the health effects of exposure to air pollution, and to identify possible sources of heterogeneity among studies. We also aimed to evaluate whether the associations varied by study design, geographic area and type of pollutant. This approach can strengthen the statistical power and generalizability of our findings, and thus help resolve inconsistencies from seemingly divergent individual study estimates.

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232 Fig. 1 Flow diagram of study selection process

M. Janghorbani et al.

Excluded for study design or irrelevant to current study (n=252)

Studies found (n=280)

Studies found on the basis of title/abstract for evaluating by reading full text (n=28)

Excluded (n=11) Irrelevant to current study (n=2) Type 1 diabetes (n=2) Gestational diabetes (n=1) No odds ratio, confidence interval or standard error reported (n=1) Review, comment, letter (=5)

Total observational studies included (n=17)

Materials and methods The present systematic review was done in accordance with the Metaanalysis Of Observational Studies in Epidemiology (MOOSE) guidelines for reviews of observational studies [27].

[28, 29]. We also excluded one cross-sectional study [30] because it reported only percentage increases in diabetes prevalence per 10-lg/m3 increase in exposure to fine particulate matter (PM2.5) without odds ratios, confidence intervals (CI) or standard errors. Data abstraction

Search strategy A literature search of online databases (PubMed, ISI, EMbase, Google Scholar and Cochrane Collaboration) through January 2013 was performed using the terms ‘‘air pollution’’ combined with ‘‘diabetes mellitus’’, ‘‘diabetes’’ or ‘‘glucose’’. The search was limited to observational studies in humans. We also reviewed the reference lists of identified publications for additional pertinent studies. No language restrictions were imposed. Eligibility criteria Published studies were included in the metaanalysis if they met the following criteria: (1) peer-reviewed original article, (2) cohort, case-crossover, time-series, case–control or cross-sectional study, and (3) adult human population. Studies were excluded if they did not provide data that allowed us to calculate standard errors for effect estimates. Figure 1 shows a flow diagram describing the study selection process. The initial search by key word yield 280 reports, of which 252 were excluded due to not eligible study design or irrelevant to the original research question. Additional 11 studies were excluded because the disease of interest was either type 1 or gestational diabetes, did not report odds ratio, confidence interval or standard error or found irrelevant to the original research question. We excluded two case–control studies because they reported exposure to air pollution and type 1 diabetes in children

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The following characteristics were recorded for each study included: publication reference (first author’s last name, year of publication and country of the study population), study design, number of participants and cases of diabetes, followup period (for cohort studies), age, gender, risk estimates with their corresponding CI, and variables controlled for in the multivariate model. For each study we extracted the risk estimates that reflected the greatest degree of control for potential confounders. For each study, the data were independently extracted by two investigators (MJ and FM), and if their evaluations differed, the discrepancy was resolved by discussion. For case-crossover studies [12, 13, 19, 24–26, 31] and two time-series analyses [20, 23] that reported percent increases in the risk of mortality, we converted these data to the mortality rate ratio (MRR) with the percent increase in risk of mortality/100 ? 1, so that multiple studies could be combined in the metaanalysis. Study quality was assessed based on design, exposure characterization and adjustment for covariates, and sensitivity analyses were conducted where feasible based on these factors. The study protocol was approved by the Institutional Review Board of Isfahan University of Medical Sciences, Iran. Statistical analysis Three measures of association were used for the metaanalysis: odds ratio (OR) (cross-sectional studies), incidence rate ratio or hazard ratio (cohort studies) and MRR

Air pollution and risk of diabetes

(case-crossover and time-series studies) [32]. For simplicity, we report OR and rate ratio as the relative risk (RR). Because the frequency of diabetes incidence and prevalence is relatively low, the OR in cross-sectional studies and incidence rate ratios in cohort studies yield similar estimates of RR [33]. We also used MRR in case-crossover and time-series studies. For categorical data, the highest and lowest category of exposure to air pollution was compared. For continues data, the RR for diabetes for a 10-lg/m3 increase in air pollutant or interquartile range (IQR) increase in air pollutant was compared. We produced forest plots to assess the multivariate adjusted RR and MRR and corresponding 95 % CI visually across studies. The logarithm of the RR and MRR with its standard error was used in the present metaanalysis. Summary RR and MRR estimates with their corresponding 95 % CI were calculated with the method of DerSimonian and Laird [34] by using random effects models. The method of DerSimonian and Laird is the simplest and most commonly used method for fitting random effects models in metaanalyses. Summary RR and MRR estimates from the random effects models were used to consider betweenstudy variability, because the tests for heterogeneity were statistically significant in all analyses. Statistical heterogeneity of the RR and MRR between studies was evaluated with Cochran’s Q test and quantified with the I2 statistic [35] (I2 = 0 % indicates no observed heterogeneity I2 C 50 % indicated substantial heterogeneity [36]). To identify sources of heterogeneity, we conducted subgroup analyses. Sensitivity analysis was done by successively removing a particular study or group of studies (if any) which had the highest impact on the heterogeneity test. Publication bias was assessed by visual inspection of the funnel plot [37]. In these funnel plots the RR and MRR were plotted against the inverse of the square of the standard error (a measure of precision). Asymmetry of the funnel plots was assessed formally with Egger’s regression asymmetry tests and adjusted rank correlation tests [38]. The reported P values are from the intercept in the regression analysis, which provides a measure of asymmetry. In addition, Begg’s adjusted rank correlation test and the trim-and-fill method were used [38, 39]. All statistical analyses were done with Stata version 11.2 software (Stata Corp, College Station, TX, USA). All P values were two-sided with a significance level less than 0.05.

Results Study characteristics A total of 29 effect estimates from 17 independent studies with 4,381,503 participants and 98,537 cases of diabetes

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from seven countries met the predefined inclusion criteria. Of these 17 studies, two were cross-sectional, examined the long-term effects of exposure to air pollution, and used OR as the measure of RR, and three were time-series studies of diabetes-related mortality that examined the short-term effects and reported percent increases in risk of mortality, which we converted to MRR (Table 1). Six were cohort studies that used incidence rate ratios or hazard ratio as the measure of RR (Table 2), and six were case-crossover studies that used percent increases in risk of mortality, which we converted to MRR (Table 3). Seven studies were conducted in the United States of America, seven in Europe, one in Asia and two in Canada. Nitrogen dioxide (NO2) was the independent variable in nine, particulate matter (diameter B 10 lm) (PM10) and PM2.5 in four, sulfur dioxide (SO2) and sulfate (SO4) in two, ozone (O3) in four, and carbon monoxide (CO), nitrogen oxide (NOx), PM10-2.5, black carbon and coefficient of haze in one study (Tables 1, 2, 3). The last five studies excluded from type of pollutant analysis. The units of effect estimates in the individual studies that used pollutants as continues variables were different, i.e., per 10 lg/m3 or per IQR (Tables 1, 2, 3). Exposure to air pollution and risk of diabetes-related mortality and morbidity The individual study results and the overall summary results for the 24 effect estimates from two cross-sectional, three time-series, six case-crossover and six cohort studies of exposure to different pollutants and diabetes are shown in Fig. 2. Thirteen of these 24 effect estimates found a statistically significant positive association between at least one air pollutant and diabetes-related mortality or morbidity. The summary measure of association (95 % CI) for all 24 effect estimates from 17 studies was 1.03 (1.02, 1.05). Heterogeneity among studies was found (I2 = 76.5 %; Pheterogeneity \ 0.001). The cohort studies contributed the most heterogeneity. In an analysis restricted to the NO2, six of nine effect estimates found a statistically significant association between NO2 and diabetes (RR range 1.01–2.15); the summary RR (95 % CI) for all nine effect estimates combined was 1.05 (1.02, 1.08). Heterogeneity among studies was significant (I2 = 71.9; Pheterogeneity \ 0.001). The time-series studies by Kan et al. [21] and Goldberg et al. [20] and cohort study by RaaschouNielsen et al. [14] contributed the most heterogeneity. In an analysis excluding these studies, the association between exposure to NO2 and diabetes did not change and the test for heterogeneity was not statistically significant (I2 = 45.8 %; Pheterogeneity = 0.117). In two effect estimates upper quartile of NO2 compared with lower quartile. The summary measure of association (95 % CI) for these

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123

M/F, 50-75

M/F C 40

M/F, all ages

M/F C 65

M/F, all ages

Dijkema et al. [22], The Netherland

Brook et al. [3], Canada

Ostro et al. [23], USA

Goldberg et al. [20], Canada

Kan et al. [21], China

Time-series

Time-series

Time-series

Crosssectional

Crosssectional

Study type

Shanghai diabetes mortality study: all individuals who lived in Zhabei district of Shanghai and died from diabetes from 2001 to 2003

Nine heavily populated California counties using data from 1999 to 2002 All residents of Montreal, Quebec aged C 65 years who died (102,148) in the metropolitan area of Montreal, during the period from 1984 to 1993 and who were registered with the universal Quebec Health Insurance Plan

Patients who attended two respiratory clinics in Hamilton: 5,228 participants (2,306 men and 2,922 women) and in Toronto: 2,406 participants (1,146 men and 1,260 women)

Semirural area of Westfriesland in northwest Netherlands: 8,018 participants (3,949 men and 4,069 women)

Study population and no. of participants

a

NO2: Lowest quartile = referent

Men: 330 Women: 289

Total deaths: 434

Total deaths: 2,947

SO2: 1.011 (0.990, 1.032) NO2: 1.013 (1.000, 1.026)

PM10: 1.006 (1.000, 1.012)

MRR: per 10-lg/m3 incraese

O3: 1.08 (0.99, 1.18)

CO: 1.08 (1.01, 1.16)

NO2: 1.12 (1.04, 1.20)

SO2: 1.10 (1.03, 1.17)

SO4: 1.05 (1.00, 1.11)

SO4: 1.05 (1.01, 1.10)

PM2.5: 1.08 (1.02, 1.15)

Coefficient of haze (COH): 1.08 (1.01, 1.16)

MRRa: per IQR increase

PM2.5: 1.02 (1.006, 1.042)a

MRRa: per 10-lg/m3 increase

Female: 1.04 (1.00, 1.08) Both genders and both clinics: 1.0 (0.98, 1.049

Women: 630

–

OR: per 2-lg/m3 increase NO2 Male: 0.99 (0.95, 1.03)

Total: 1,252 Men: 622

long-term trends, season, weather variables, and day of the week

Time, seasonality, temperature, humidity, day of the week Seasonal and subseasonal trends, calendar year, dayof-the-week, and weather variables

Age, BMI, neighborhood income

Age, gender, neighbourhood income

OR:

Total: 619

Third quartile = 1.25 (0.98, 1.58)

Controlled variables

OR or MRR (95 % CI)

No. of diabetes

Percent increase in risk of mortality converted to mortality rate ratio, and percent increase in risk of mortality/100 ? 1

OR odds ratio, MRR mortality rate ratio, CI confidence interval, IQR interquartile range

Gender, age (years)

Source, country

Table 1 Time-series and cross-sectional studies of exposure to air pollution and risk of diabetes that met the eligibility criteria for inclusion in the systematic review and metaanalysis

234 M. Janghorbani et al.

13.0

10.0

16.0

13.0

9.7

5.6

Raaschou-Nielsen et al. [14], Denmak

Coogan et al. [15], USA

Kramer et al. [4], Germany

Puett et al. [5], USA

Anderson et al. [40], Denmark

Zanobetti et al. [16], USA

M/F, 76

M/F, 56.1

Nurse’s Health Study (NHS): 55.1

Health Professional Follow-Up Study (HPFS): 57.3

M/F

F, 54–55

F, 21–69

M/F, 50–64

Gender, age at enrollment (years)

Four cohorts of persons with specific diseases in 105 United States cities: 2,935,647

Danish Diet, Cancer, and Health cohort in the Danish National Diabetes: 51,818 (24,545 men and 27,273 women)

Two prospective cohorts, the Nurse’s Health Study and the Health Professional Follow-Up Study: 74,412 women (NHS) and 15,048 men (HPFS)

The prospective Study on the Influence of Air Pollution on Lung, Inflammation and Aging (SALIA) cohort study: 1,775

Black Women’s Health Study: 3,992

Danish diet, cancer and health cohort: 52,061 participant

Study population and no. of participants

RR relative risk, CI confidence interval, T2DM type 2 diabetes mellitus, IQR interquartile range

Average followup period (years)

Source, country

T2DM deaths: 1,134

Women: 1,175

Men: 1,702

Total: 2,877

T1 and T2DM:

T2DM: 3,784 (NHS) and 688 (HPFS)

T2DM: 187

T2DM: 183

T1 and T2DM death: 122

No. and type of diabetes

O3: 1.07 (1.05, 1.10)

Diabetes survival rate ratio per 10-lg/m3 increase

Women: 1.07 (1.01, 1.13)

Men: 1.01 (0.97, 1.07)

Total: 1.04 (1.00, 1.08)

NO2:

Diabetes incidence rate ratio per IQR increase

PM10: 1.04 (0.99, 1.09) PM10–2.5: 1.04 (0.99, 1.09)

PM2.5: 1.03 (0.96, 1.10)

Diabetes incidence rate ratio per IQR increase

PM10: 1.16 (0.81, 1.65)

N02: 1.34 (1.02, 1.76)

Diabetes incidence rate ratio per IQR increase

NOx: 1.25 (1.07, 1.46) per IQR increase

Diabetes incidence rate ratio per 10-lg/m3 increase PM2.5:1.63 (0.78, 3.44)

Lower quartile = referent Upper quartile = 2.15 (1.21–3.83)

NO2:

Diabetes mortality rate ratio

RR (95 % CI)

Individual risk factors, season, temperature, city-specific longterm time trends

Gender, BMI, waist-to-hip ratio, smoking status, smoking duration, smoking intensity, environmental tobacco smoke, education, physical activity, alcohol consumption, fruit consumption, fat consumption, calendar year, hypertension, hypercholesterolemia, myocardial infarction

Age, season, calendar year, state of residence, cigarette smoking, hypertension, BMI, alcohol intake, physical activity, diet

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

Age, BMI, education, income, smoking, alcohol consumption, exercise, neighborhood socioeconomic status, family history of diabetes

Sex, age, calendar year, education, occupation, smoking, hypertension treatment, physical activity

Controlled variables

Table 2 Cohort studies of exposure to air pollution and risk of diabetes that met the eligibility criteria for inclusion in the systematic review and metaanalysis

Air pollution and risk of diabetes 235

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Table 3 Case-crossover studies of exposure to air pollution and risk of diabetes that met the eligibility criteria for inclusion in the systematic review and metaanalysis Source, country

Gender, age (years)

Study population and no. of participants

No. of diabetes death

Mortality rate ratio (95 % CI)a

Controlled variables

Maynard et al. [24], USA

M/F, 76.6

Hospital deaths in the Boston metropolitan area in the years 1995–2002: 107,925 deaths (46,377 men and 61,548 women)

2,694

MRR per IQR increase Black carbon (BC): 1.06 (0.98, 1.37)

Month-long control period

All natural deaths among adult residents of 9 Italian cities between 1997 and 2004: 321,024 deaths

30,173

Elderly residents of Cook County, Illinois, who had a history of hospitalization for heart or lung disease: 65,180

12,978

All residents of 10 Italian cities who died from natural cause between 2001 and 2005: 127,860

4,000

All non-accident deaths from 3 counties in eastern Massachusetts between 1995 and 2002: 157,197

3,845

Natural deaths among residents of 10 Italian cities between 2001 and 2005: 276,205

30,620

Forastiere et al. [31], Italia

Bateson et al. [25], USA

Stafoggia et al. [12], Italia

Ren et al. [13], USA

Chiusolo et al. [19], Italia

M/F, [35

M/F, C65

M/F, C35

M/F, C35

M/F, [35

SO4: 1.03 (0.97, 1.10) MRR per 10-lg/m3 increase

Time, population changes, meteorological conditions

PM10: 1.01 (1.00, 1.02) MRR per 10-lg/m3 increase PM10: 1.01 (1.00, 1.03) MRR per 10-lg/m3 increase

Temperature, humidity, barometric pressure, day of the week

Age, weight

O3: 1.06 (1.01, 1.10) MRR per 20- lg/m3 increase O3: 1.08 (1.01, 1.16) MRR per 10-lg/m3 increase NO2: 1.04 (1.02, 1.06)

Age, gender, race, education, marital status, population density, income, temperature, day of the week Barometric pressure, temperature, day of the week

CI confidence interval, IQR interquartile range, MMR mortality rate ratio a

Percent increase in risk of mortality converted to mortality rate ratio, and percent increase in risk of mortality/100 ? 1

two effect estimates was 1.36 (1.08, 1.70). In three effect estimates RR increased per 10-lg/m3. The summary measure of association (95 % CI) for these three effect estimates was 1.02 (099, 1.05) increase per 10-lg/m3. The summary measure of association (95 % CI) for four effect estimates used increase per IQR was 1.09 (1.04, 1.14). In an analysis restricted to the O3, three of four effect estimates found a statistically significant association between O3 and diabetes (RR range 1.06–1.08); the summary RR (95 % CI) for all four effect estimates combined was 1.07 (1.05, 1.09) and the test for heterogeneity was not statistically significant (I2 = 00.0 %; Pheterogeneity = 0.961). In three effect estimates RR increased per 10-lg/m3 O3 and in one effect estimate increase per IQR. The summary measure of association (95 % CI) for three effect estimates was 1.08 (0.99, 1.18) increase per 10-lg/m3 O3. In an analysis restricted to the SO2 and SO4, two of four effect estimates found a statistically significant association between SO2 and SO4 and diabetes; the summary RR (95 % CI) for two SO2 effect estimates combined was 1.05

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(0.97, 1.14) and for two SO4 effect estimates was 1.05 (1.01, 1.08). Heterogeneity among SO2 and SO4 studies was not found. In one effect estimates RR increased per 10-lg/m3 SO2 and in one SO2 and both SO4 RR increased per IQR. Regarding gaseous pollutants, when all 17 effect estimates from 13 studies were analyzed together, gaseous pollutants were associated with an increased risk of diabetes (summary RR (95 % CI) 1.05 (1.03, 1.07)). There was statistically significant heterogeneity among studies (I2 = 69.7 %; Pheterogeneity \ 0.001). The time-series studies by Kan et al. [21] and Goldberg et al. [20] contributed the most heterogeneity. In an analysis excluding these studies, the association between gaseous pollutants and diabetes became slightly weaker (summary RR (95 % CI) 1.03 (1.01, 1.05)), and the test for heterogeneity was not statistically significant (I2 = 47.7 %; Pheterogeneity = 0.063). With regard to exposure to particulate matter pollutants (PM10 or PM2.5) and diabetes-related mortality or morbidity risk, of eight effect estimates, two time-series studies

Air pollution and risk of diabetes

Air Study pollutant design Cross-sectional Dijkema et al. (2011) [22] NO2 Cross-sectional NO2 Brook et al. (2008) [3] Time-series Goldberg et al. (2006) [20] NO2 Time-series Kan et al. (2004) [21] NO2 Raaschou-Nielsen et al. (2013) [14] Cohort NO2 Coogan et al. (2012) [15] Cohort NO2 Cohort NO2 Kramer et al. (2010) [4] Cohort Anderson et al. (2012) [40] NO2 Case-crossover NO2 Chiusolo et al. (2011) [19] Combined effect Time-series Goldberg et al. (2006) [20] O3 Zanobetti et al. (2011) [16] Cohort O3 Case-crossover Stafoggia et al. (2010) [12] O3 Case-crossover O3 Ren et al. (2010) [13] Combined effect Kan et al. (2004) [21] Time-series PM10 Cohort Puett et al. (2011) [5] PM10 Case-crossover PM10 Forastiere et al. (2008) [31] Bateson et al. (2004) [25] Case-crossover PM10 Combined effect Ostro et al. (2006) [23] Time-series PM2.5 Goldberg et al. (2006) [20] Time-series PM2.5 Coogan et al. (2012) [15] Cohort PM2.5 Kramer et al. (2010) [4] Cohort PM2.5 Combined effect Goldberg et al. (2006) [20] Time-series SO2 Kan et al. (2004) [21] Time-series SO2 Combined effect Goldberg et al. (2006) [20] Time-series SO4 Maynard et al. (2007) [24] Case-crossover SO4 Combined effect Source

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RR or MMR (95% CI) 1.250 (0.984, 1.587) 1.010 (0.990, 1.030) 1.120 (1.043, 1.203) 1.011 (0.990, 1.032) 2.150 (1.208, 3.825) 1.250 (1.070, 1.460) 1.340 (1.020, 1.760) 1.040 (1.001, 1.081) 1.040 (1.020, 1.060) 1.049 (1.018, 1.081) 1.080 (0.989, 1.179) 1.070 (1.050, 1.090) 1.060 (1.010, 1.113) 1.080 (1.008, 1.157) 1.070 (1.052, 1.088) 1.006 (1.000, 1.012) 1.040 (0.991, 1.091) 1.010 (1.000, 1.020) 1.010 (1.000, 1.020) 1.008 (1.003, 1.013) 1.020 (1.006, 1.034) 1.080 (1.017, 1.147) 1.630 (0.776, 3.423) 1.160 (0.813, 1.656) 1.046 (0.993, 1.012) 1.100 (1.032, 1.172) 1.013 (1.000, 1.026) 1.049 (0.969, 1.136) 1.050 (1.012, 1.090) 1.030 (0.967, 1.097) 1.045 (1.012, 1.079)

P-value 0.067 0.325 0.002 0.302 0.009 0.005 0.035 0.046 0.000 0.002 0.086 0.000 0.019 0.029 0.000 0.049 0.110 0.049 0.049 0.001 0.005 0.012 0.197 0.414 0.093 0.003 0.049 0.239 0.010 0.357 0.007

0.5

1

2

Fig. 2 Forest plot of the association between exposure to air pollution and diabetes-related risk in cross-sectional, time-series, cohort, and case-crossover studies of different air pollutants. RR relative risk, MRR mortality rate ratio, CI confidence interval, square

study-specific RR or MRR estimate, horizontal line 95 % CI, diamond summary RR or MRR estimate and its corresponding 95 % CI. All statistical tests were two-sided

found a statistically significant association between PM2.5 and diabetes-related mortality [20, 23]. Overall, the random effects model summarizing all four comparisons for PM2.5 and four comparisons for PM10 suggested a statistically significant association for PM10 but not for PM2.5; the summary RR was 1.008 (95 % CI: 1.003, 1.01) for PM10 and 1.05 (95 % CI: 0.99, 1.10) for PM2.5. Heterogeneity among studies was not found (I2 = 00.0 %; Pheterogenefor PM10 and I2 = 43.0 %; Pheterogeneity = 0.582 ity = 0.151 for PM2.5). As expected the results was almost similar when we used effect estimate per IQR or per 10-lg/ m3 increase. In an analysis restricted to the cohort studies, four of 10 effect estimates from six cohort studies found a statistically significant association between at least one pollutant and diabetes (RR range 1.07–2.15); the summary RR (95 % CI) for all six cohort studies combined was 1.06 (1.03, 1.10). Heterogeneity among studies was significant (I2 = 49.8; Pheterogeneity = 0.036). The cohort study by Raaschou-Nielsen et al. [14] contributed the most heterogeneity. In an analysis excluding this study, the association between exposure to air pollution and diabetes did not change (summary RR (95 % CI) 1.06 (1.03, 1.08), and the test for

heterogeneity was not statistically significant (I2 = 33.9 %; Pheterogeneity = 0.146). In an analysis restricted to the case-crossover studies, three of seven effect estimates from six case-crossover studies found a statistically significant association between at least one pollutant and diabetes-related mortality (MRR range 1.04–1.08); the summary MRR (95 % CI) for all six case-crossover studies combined was 1.03 (1.01, 1.04). Heterogeneity among studies was significant (I2 = 59.5; Pheterogeneity = 0.022). The case-crossover study by Chiusolo et al. [19] contributed the most heterogeneity. In an analysis excluding this study, the association between exposure to air pollution and diabetes-related mortality did not change (summary MRR (95 % CI) 1.02 (1.00, 1.04), and the test for heterogeneity was not statistically significant (I2 = 42.4 %; Pheterogeneity = 0.123). In an analysis restricted to the three time-series studies, six of ten effect estimates found a statistically significant association between at least one pollutant and diabetesrelated mortality (MRR range 1.02–1.12); the summary MRR (95 % CI) for all three time-series studies combined was 1.03 (1.02, 1.05). Heterogeneity among studies was not found (I2 = 33.1; Pheterogeneity = 0.164).

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Table 4 Summary of relative risk (RR) or mortality rate ratio (MRR) estimates (95 % confidence intervals (CI)) for cross-sectional, cohort, time-series and case-crossover studies of the association between exposure to air pollution and risk of diabetes by study design, geographic area, type of pollutant and type of variables Subgroup

No. of studies

Summary RR or MRR (95 % CI)

Between studies I2 (Pheterogeneity)

Between subgroups Q (Pheterogeneity)

Geographic area United States and Canada

9

1.05 (1.03–1.07)

61.1 % (0.001)

Europe

7

1.04 (1.01–1.08)

72.4 % (0.001)

Asia

1

1.01 (1.00–1.01)

0.0 % (0.595)

NO2

9

1.04 (1.01–1.08)

71.9 % (0.001)

O3

4

1.07 (1.05–1.09)

0.0 % (0.961)

PM2.5

4

1.05 (0.99–1.10)

43.0 % (0.151)

PM10

4

1.01 (1.003–1.013)

0.0 % (0.582)

SO4

2

1.04 (1.012, 1.079)

0.0 % (0.606)

SO2

2

1.05 (1.012, 1.079)

0.0 % (0.613)

Cross-sectional

2

1.08 (0.891, 1.323)

67.0 % (0.081)

Time-series Cohort

3 6

1.03 (1.013, 1.043) 1.08 (1.031, 1.126)

73.0 % (0.001) 58.0 % (0.021)

Case-cross over

6

1.02 (1.009, 1.039)

65.0 % (0.013)

24.79 (0.001)a

Type of pollutant 44.31(0.001)

Type of studies 102.07 (0.001)

Type of pollutant and type of variables NO2 categorical

2

1.36 (1.08, 1.70)

65.6 % (0.088)

NO2 per 10-lg/m3 increase

3

1.02 (0.99, 1.05)

63.9 % (0.063) 70.4 % (0.018)

NO2 per IQR increase

4

1.09 (1.04, 1.14)

O3 per 10-lg/m3 increase

3

1.07 (1.05, 1.09)

0.0 % (0.901)

O3 per IQR increase

1

1.08 (0.99, 1.18)

0.0 % (0.990)

3

PM2.5 per 10-lg/m increase

2

1.02 (0.97, 1.08)

34.8 % (0.216)

PM2.5 per IQR increase

2

1.06 (1.00, 1.12)

4.7 % (0.306)

PM10 per 10-lg/m3 increase

3

1.008 (1.003, 1.012)

0.0 % (0.699)

PM10 per IQR increase

2

1.04 (0.99, 1.09)

0.0 % (0.551)

SO2 per 10-lg/m3 increase

1

1.01 (1.00, 1.03)

0.0 % (0.999)

SO2 per IQR increase

1

1.10 (1.03, 1.17)

0.0 % (0.999)

SO4 per IQR increase

2

1.05 (1.01, 1.08)

0.0 % (0.606)

29.82 (0.001)

All statistical tests were two-sided RR relative risk, MRR mortality rate ratio, IQR interquartile range, CI confidence interval a

Test for heterogeneity between combined United States and Canada, Europe and Asia

In an analysis restricted to the two cross-sectional studies, none of the effect estimates was statistically significant; the summary RR (95 % CI) for both cross-sectional studies combined was 1.09 (0.90, 1.32). Heterogeneity between the studies was not found (I2 = 65.1; Pheterogeneity = 0.091). However, the summary estimate was statistically significant in time-series, case-crossover and cohort studies. Subgroup metaanalysis was done by geographic area (Table 4). With regard to geographic area, exposure to air pollution seemed to be a greater risk factor for diabetes in North American (pooled RR 1.05) and European populations (pooled RR 1.04) than in Asian populations (RR 1.01)

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(one study only). Subgroup metaanalysis was also done by type of pollutant and unit of RR increase (Table 4). As expected the results was almost similar when we used effect estimate as categorical or continues variables (per IQR or per 10-lg/m3 increase).

Publication bias There was evidence of publication bias for the association between exposure to air pollution and diabetes risk (P = 0.009 for Begg’s adjusted rank correlation test and P \ 0.001 for Egger’s regression asymmetry test), but no

Air pollution and risk of diabetes

missing studies were identified with the trim-and-fill method.

Discussion The findings from this metaanalysis indicate that both gaseous pollutants and PM were weakly associated with a higher risk of diabetes-related mortality and morbidity; the association with gaseous pollutants, particularly NO2 and O3, was strongest. Our results were consistent for case-crossover, time-series and cohort studies, and for studies carried out in North America and Europe. To the best of our knowledge, this is the first systematic review and metaanalysis of observational studies to assess the effect of exposure to air pollution on diabetes risk and susceptibility. Despite differences in the pollutants, age groups and study designs, most of the studies we included showed weak evidence of an association between diabetes and air pollution. For example, the cross-sectional study by Brook et al. [3] reported a 4 % increase in diabetes prevalence among women, but not men, who were exposed to higher levels of NO2. One cross-sectional study linked diabetes prevalence to PM2.5 [30] but another such study found no consistent association [22]. Some time-series and case-crossover studies have linked short-term exposure to air pollution with diabetes-related death [21, 23, 24], and data from prospective cohort studies of long-term exposure to air pollution and diabetes collectively indicate a weak association between traffic-related air pollution and the incidence of type 2 diabetes [4, 5, 40]. Evidence of associations between air pollution exposures and diabetes mortality is also somewhat inconsistent. In a time-series study of 434 diabetes-related deaths in Shanghai, Kan et al. [21] found a very weak association with PM10 and NO2. In a study of 3,677 deaths [20] in Canada, some associations were reported between daily mortality among people with diabetes and the 3-day mean during the warm or cold season for certain air pollutants generated from combustion sources, in individuals who also had cardiovascular disease, cancer or respiratory disease. No associations were reported for individuals without these conditions. In a case-crossover study of 100,000 deaths from 1995 to 2002, an interquartile increase in traffic particle exposure the day before death was associated with a 5.7 % increase in deaths due to diabetes [24]. Recently, Brook et al. [11], in a cohort study of 2.1 million adults, evaluated the association between long-term exposure to ambient fine particulate matter (PM2.5) and diabetes-related mortality, and reported that long-term exposure to PM2.5, even at low levels, was related to an increased risk of mortality attributable to diabetes. Ambient air pollution comprises a complex mixture of pollutants, and it is impossible to separate the effects of one individual pollutant from those of others. The limitations of

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single-pollutant models in explaining health consequences have been recognized [41, 42]. This makes it desirable to expand the number of pollutants measured with a given study design [42, 43]. We believe that the linkage we observed in this metaanalysis between diabetes and exposure to ambient air pollution is likely due to the mixture of air pollutants rather than just one component. To shed further light on this relationship, studies should be designed to examine the impact of the interaction between these various measures on functional outcomes in diabetes. Although the exact mechanisms whereby exposure to air pollution is associated with a slightly higher diabetes risk are not entirely clear, several mechanisms can be proposed. Like those proposed to explain the associations between exposure to air pollution and cardiovascular disease, the weak association between exposure to air pollution and a higher susceptibility or risk of diabetes may also be attributable to oxidative stress, inflammation [44–48], elevated HbA1c [48, 49], elevated blood pressure [50], impaired vascular endothelial function [48, 51, 52], changes in brown adipose tissue function [48, 53] and alterations in autonomic tone [46, 54], which may increase insulin resistance [8]. There is also evidence that changes in the adipocytokines adiponectin, resistin and leptin over time may contribute to the association between exposure to air pollution and diabetes [8, 48]. Although a number of plausible pathways related to these mechanisms, particularly inflammation and oxidative stress, have been proposed, the mechanisms underlying the observed associations remain unclear, both because of the complex nature of diabetes etiology and the heterogeneous composition of air pollution. Moreover, no studies to date have examined the effect of air pollution exposure on b-cell function. Published studies on the association between diabetes and air pollution exposure are currently limited and have very different characteristics and interpretations, so our analysis must be interpreted in the context of the limitations in the available data. Different studies have investigated long-term and short-term effects, incidence measures, cause-specific mortality and overall mortality in patients with diabetes, and different types of pollutants. They pertain to very different biological processes, and to combine and interpret them in a single estimate of the risk of diabetes is problematic since different mechanisms are likely to be involved with different pollutants. As in any metaanalysis, the possibility of publication bias is a concern; papers reporting a positive association may be more likely to be submitted or accepted for publication. The results obtained from funnel plot analyses and formal statistical tests provided evidence for this bias, and the possibility of some degree of publication bias cannot be ruled out. Thus, our findings are likely to be biased toward an

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overestimation of the true effect, although the degree of publication bias is difficult to quantify. In most studies included in our metaanalysis, only diagnosed cases of diabetes were considered, with no distinction between type 2 diabetes and type 1. Because [90–95 % of patients with diabetes have type 2 disease, the associations probably describe the effects on type 2 diabetes. In addition, because type 2 diabetes is an underdiagnosed disease, some degree of misclassification of exposure to diabetes is likely to have occurred. This type of nondifferential misclassification would tend to attenuate the true relationship between exposure to air pollution and type 2 diabetes. All evidence reported to date comes from cohort, cross-sectional, time-series or case-crossover observational studies, making it difficult to draw conclusions regarding causality. In fact, the issue of an effect of air pollution exposure on the risk of diabetes was raised by observational studies, which may be biased by uncontrolled confounders. All observational epidemiological studies are subject to potential confounding, but different study designs are usually subject to different types of confounding, or confounding by different sets of covariates. Case-crossover studies used time-series analyses to examine the association between day-to-day changes in daily diabetes mortality counts and day-to-day changes in air pollution concentrations. These studies are usually done separately in different geographic areas, and including them in a single metaanalysis can help to reduce confounding by factors that vary across areas. Cohort analyses of long-term exposure to air pollution and diabetes are different. In these studies, long-term average air pollution concentrations in a particular geographic setting are calculated for the population in that setting, and their diabetes experience across settings is compared with variations in air pollution across settings. In this case seasonal and other short-term fluctuations are not a source of confounding, because the type of exposure considered is long-term exposure across settings. The potential confounders, therefore, involve those factors that also vary across settings. These include factors such as diet and socioeconomic status—which are precisely the factors that are not confounders in time-series studies. However, a positive association between exposure to air pollution and diabetesrelated risk remained when we restricted the metaanalysis according to study design, geographic area and type of pollutants. Exposure to air pollution may also vary among population subgroups according to socioeconomic status, educational level, air conditioning use, proximity to roadways, geographic location, level of physical activity and work environment. Differences in patient characteristics, age, gender, ethnicity and smoking can also act as confounding factors in the relationship between exposure to air pollution

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M. Janghorbani et al.

and diabetes. Thus, in observational studies, individuals with diabetes may differ, so adjustments for known confounders can only reduce biases, but not eliminate them. Moreover, unknown confounders cannot be adjusted for. Many chronic health conditions appeared to increase susceptibility to the effects of exposure to air pollution; in particular people with a history of cardiovascular conditions appear to be at higher risk. Thus, the observed minimal increase in the risk of diabetes associated with exposure to air pollution may reflect confounding by these risk factors. In addition, a further limitation in our study was selection and information bias arising from methodological heterogeneity among the studies we included. Although residual confounding may be present because of imperfect measurement of exposure to air pollution in different studies, it is unlikely to account for more than a portion of the remaining excess risk. Finally, most published papers were from the North American and Western European populations; limited data are available for other populations, particularly from nonwestern populations. In conclusion, the results of out metaanalysis support a weak positive association between exposure to air pollution and diabetes. The evidence was too limited and inconclusive to draw a robust conclusion about the association of exposure to air pollution with diabetes, in part because of inconsistencies in study designs and potentially selective reporting of the results for some of the pollutants that were measured. Acknowledgments We thank K. Shashok (AuthorAID in the Eastern Mediterranean) for improving the use of English in the manuscript. This work was partially supported by funds from Isfahan University of Medical Sciences, Iran. This research was performed as a part of the academic activity of the university. Conflict of interest

None.

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