We examined the relationship of PM 2·5 and the risk of incident diabetes in a longitudinal cohort of 1 729 108 participants followed up for a median of 8·5 years (IQR 8·1–8·8). In adjusted models, a 10 μg/m 3 increase in PM 2·5 was associated with increased risk of diabetes (HR 1·15, 95% CI 1·08–1·22). PM 2·5 was associated with increased risk of death as the positive outcome control (HR 1·08, 95% CI 1·03–1·13), but not with lower limb fracture as the negative outcome control (1·00, 0·91–1·09). An IQR increase (0·045 μg/m 3 ) in ambient air sodium concentration as the negative exposure control exhibited no significant association with the risk of diabetes (HR 1·00, 95% CI 0·99–1·00). An integrated exposure response function showed that the risk of diabetes increased substantially above 2·4 μg/m 3 , and then exhibited a more moderate increase at concentrations above 10 μg/m 3 . Globally, ambient PM 2·5 contributed to about 3·2 million (95% uncertainty interval [UI] 2·2–3·8) incident cases of diabetes, about 8·2 million (95% UI 5·8–11·0) DALYs caused by diabetes, and 206 105 (95% UI 153 408–259 119) deaths from diabetes attributable to PM 2·5 exposure. The burden varied substantially among geographies and was more heavily skewed towards low-income and lower-to-middle-income countries.

We did a longitudinal cohort study of the association of PM 2·5 with diabetes. We built a cohort of US veterans with no previous history of diabetes from various databases. Participants were followed up for a median of 8·5 years, we and used survival models to examine the association between PM 2·5 and the risk of diabetes. All models were adjusted for sociodemographic and health characteristics. We tested a positive outcome control (ie, risk of all-cause mortality), negative exposure control (ie, ambient air sodium concentrations), and a negative outcome control (ie, risk of lower limb fracture). Data for the models were reported as hazard ratios (HRs) and 95% CIs. Additionally, we reviewed studies of PM 2·5 and the risk of diabetes, and used the estimates to build a non-linear integrated exposure response function to characterise the relationship across all concentrations of PM 2·5 exposure. We included studies into the building of the integrated exposure response function if they scored at least a four on the Newcastle-Ottawa Quality Assessment Scale and were only included if the outcome was type 2 diabetes or all types of diabetes. Finally, we used the Global Burden of Disease study data and methodologies to estimate the attributable burden of disease (ABD) and disability-adjusted life-years (DALYs) of diabetes attributable to PM 2·5 air pollution globally and in 194 countries and territories.

PM 2·5 air pollution is associated with increased risk of diabetes; however, a knowledge gap exists to further define and quantify the burden of diabetes attributable to PM 2·5 air pollution. Therefore, we aimed to define the relationship between PM 2·5 and diabetes. We also aimed to characterise an integrated exposure response function and to provide a quantitative estimate of the global and national burden of diabetes attributable to PM 2·5 .

The Lancet Commissionon pollution and health published its report in October, 2017, and it provided a comprehensive review of the effect of the so-called pollutome on human health. The Commission outlined a glaring deficiency in evidence and provided a set of recommendations to fill important knowledge gaps. One of the recommendations outlined by the Commission is to “define and quantify the burden of diabetes attributable to PMair pollution”.An assessment of the global and national burden of diabetes attributable to PMwould provide a better understanding of the epidemiology of diabetes, identify endemic areas, and further contribute to the global and national discussions on the hazardous effect of air pollution on diabetes. Therefore in this study, we aimed to further define the relationship of PMand diabetes, using a longitudinal cohort study design. We also aimed to characterise an integrated exposure response function, using the body of evidence on the relationship of PMpollution and diabetes; and to provide a quantitative estimate of the global and national burden of diabetes attributable to PMin 194 countries and territories, using the Global Burden of Disease (GBD) methodologies.

Taken together, the findings address the knowledge gap outlined in the Lancet Commission on pollution and health to “define and quantify the burden of diabetes attributable to PM 2·5 air pollution”. Most importantly, the study shows that substantial risk exists at concentrations well below those outlined in the air quality standards of WHO and national and international regulatory agencies. Although the non-linearity of the integrated exposure response function suggests modest reduction in risk unless PM 2·5 is decreased substantially in high-pollution areas, given the considerable number of people living in heavily polluted geographies, even incremental reductions in PM 2·5 will ameliorate the burden of diabetes. Finally, we observed that the burden of diabetes attributable to PM 2·5 exhibited substantial geographical variability, and was more skewed towards regions that are least prepared to cope with the consequences of this excess burden. The results will possibly be helpful to promote the public's awareness about the effect of PM 2·5 pollution on the risk of diabetes, and serve to inform and guide policy making aimed at addressing health consequences of environmental air pollution.

This study addresses the research recommendation and provides evidence that ambient PM 2·5 pollution is associated with increased risk of diabetes. We examined the association in a longitudinal cohort of about 1·7 million US veterans, in which we control for relevant individual-level variables and ecological characteristics. We tested a positive control, as well as negative outcome and exposure controls to address concern about spurious causal inference. The study synthesised previous evidence to build an integrated exposure response function to characterise the risk of diabetes across all PM 2·5 concentrations experienced by humans. The integrated exposure response function was non-linear in that risk increased substantially above PM 2·5 concentrations of 2·4 μg/m 3 , and then exhibited a more moderate increase in risk at concentrations above 10 μg/m 3 . Additionally, the study suggests that in 2016, there were about 3·2 million cases of incident diabetes, and about 8·2 million healthy life years lost due to diabetes attributable to air pollution. The burden varied substantially by geography and was most pronounced in less developed countries.

Previous epidemiological evidence suggests that environmental exposure to PM 2·5 is associated with risk of diabetes. However, the Lancet Commission on pollution and health identified knowledge gaps and outlined several research recommendations including the need to further “define and quantify the burden of diabetes attributable to PM 2·5 air pollution”.

Air pollution is an important global health problem.PM—the most widely studied air pollutant—is associated with increased risk of cardiovascular disease, pulmonary disease, kidney disease, and other non-communicable diseases,and contributed to about 4·2 million premature deaths in 2015.A growing body of evidence strongly suggests an association between PMpollution and the risk of diabetes.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

We did all analyses in SAS (version 7.1). We generated maps using ArcMap (version 10.5). The study was approved by the Institutional Review Board of the VA Saint Louis Health Care System (Saint Louis, MO, USA).

Uncertainty in measurements was factored in our estimations through the generation of measures from a distribution of 10 000 estimates, and the median and 95% UIs are reported. Further details on estimation and UIs are presented in the appendix (pp 14, 15) . Burden measures are reported as values, rates per 100 000 population, and age-standardised rates per 100 000 population. World maps of age-standardised ABD, YLD, YLL, and DALY rates are presented. Age-standardised DALY rates were additionally analysed by World Bank income classification and the sociodemographic index quintile.

YLD due to diabetes is a measure of the burden placed on a population due to the ill-effects of living with diabetes. YLL due to diabetes is a measure of the burden placed on a population due to dying prematurely from diabetes. The DALY due to diabetes is a summary measure of YLD and YLL, and represents the total years of healthy life lost due to ill-health, disability, or early death due to diabetes.YLD, YLL, and DALYs of diabetes due to PMwere estimated by multiplying the diabetes- specific GBD values of the corresponding measure by the PAF of diabetes due to PMexceeding the TMREL.Details of these measures are discussed in the appendix (pp 13–15)

GBD 2016 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.

GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

The population attributable fraction (PAF) of diabetes due to PMrepresents the proportion of diabetes that would be eliminated if the PMexposure was reduced to concentrations equal to or less than the TMREL. The PAF of diabetes due to PMexposure above the TMREL was calculated with a GBD 2016 equation,using risk estimates from the integrated exposure response function. The TMREL was set as a uniform distribution between 2·4 μg/mand 5·9 μg/m, for which levels under the TMREL were treated as contributing no risk.The attributable burden of disease (ABD), defined as the number of incident cases of diabetes per year attributable to PMexceeding the TMREL, was calculated using estimates of diabetes from the GBD 2016 studymultiplied by the PAF of diabetes due to PMexceeding the TMREL.

GBD 2016 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

National annual PMexposure estimates, which are population weighted and derived from the integration of satellite data, surface measurements, geographical data, and a chemical transport model, were obtained from GBD 2015.Estimates are population weighted. Incident rates, years of life lived with disability (YLD), years of life lost (YLL), and disability-adjusted life-years (DALYs) of diabetes and all causes, and their UIs were obtained from GBD 2016.The GBD methodology, explained elsewhere in detail,estimates these measures by using data from specific published literature on diabetes and mortality in hierarchical models.The GBD Population Estimates dataset provided population size.Country income classifications were obtained from the World Bank.

GBD 2016 Causes of Death Collaborators Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016.

GBD 2016 DALYs and HALE Collaborators Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.

GBD 2016 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.

GBD 2016 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.

GBD 2016 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.

GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

GBD 2016 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

The integrated exposure–response function fits available epidemiological data using a Bayesian hierarchical modelling approach, and is based on GBD methodology, which has been described elsewhere in detail.The theoretical minimum risk exposure level (TMREL) was assigned on the basis of a uniform distribution of PMfrom 2·4 μg/mto 5·9 μg/m, representing exposure values between the minimum and fifth percentiles of exposure distributions from outdoor air pollution cohort studies.TMREL by its definition should minimise individual-level and population-level risk and be theoretically possible to achieve, but not necessarily affordable or feasible to achieve.Studies were weighted using the quality effects approach.Results were obtained from 1000 sets of simulated values.The mean and 95% uncertainty intervals (UIs) are presented.

GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010.

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

Studies were included in the building of the integrated exposure response function if they scored at least a four on the Newcastle-Ottawa Quality Assessment Scale—a nine-point scale for assessing quality of cohort studies—and were only included if the outcome was type 2 diabetes or all types of diabetes. Active smoking studies were only included if they contained a recorded dose-response of cigarettes per day, which was necessary for assigning a corresponding PMexposure value, and if the reference group consisted of those who had never smoked. Passive smoking studies were included if the reference group had never smoked and were not exposed to passive smoke. Passive smoke was assigned a PMexposure of 35 μg/m, and active smoking 667 μg/mper cigarette per day.Selected studies, along with the Veterans Affairs longitudinal cohort study presented here, were included in building the integrated exposure response function; details on included studies are presented in the appendix (pp 12, 19–28)

GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

An integrated exposure response function based on GBD methodologies was built to assess the risk of diabetes due to PMacross the spectrum of PMexposure concentrations around the world.A literature review was done, where we evaluated currently available literature on the associations between risk of diabetes and PM, passive smoking, and active smoking for the use in building an integrated exposure response function.Passive smoking and active smoking were used as proxy exposures for high concentration of PM, because published literature on PMtends to be from developed countries with these values on the lower end of the spectrum, therefore, leaving a scarcity of evidence on the relationship at higher concentrations of exposure.Exposure attribution, as estimated by previous studies,is derived from breathing rate (ie, average volume of air breathed per minute), and the PMmass per cigarette, or ambient exposure due to living with someone who smokes.

GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

Smoking is associated with reduced risk of autoimmune diabetes in adults contrasting with increased risk in overweight men with type 2 diabetes.

Effects of smoking, obesity and physical activity on the risk of type 2 diabetes in middle-aged Finnish men and women.

The effect of smoking status upon occurrence of impaired fasting glucose or type 2 diabetes in Korean men. Journal of preventive medicine and public health.

A prospective study investigating the association between environmental tobacco smoke exposure and the incidence of type 2 diabetes in never smokers.

Effects of Smoking on the incidence of non-insulin-dependent diabetes mellitus: replication and extension in a Japanese cohort of male employees.

Impact of parental smoking on diabetes, hypertension and the metabolic syndrome in adult men and women in the San Antonio Heart Study.

GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

The use of a negative control is a valuable complement to other epidemiological methods and serves to identify and resolve both suspected and unsuspected sources of spurious causal inference including confounding, mismeasurements, and other biases, as well as design or analytic flaws.Ambient air sodium concentration is measured by air monitoring stations; however, there is no biological basis to support an association between sodium concentrations in the air and the risk of diabetes. Therefore, ambient air sodium is an appropriate negative exposure control.The negative outcome control was selected on the basis of the criteria outlined by Lipsitch and colleagues.There is no previous knowledge of and no biological or mechanistic plausibility to explain an association between PMand the risk of lower limb fracture. We therefore considered it a suitable negative outcome control.

We additionally curated data from the US County Health Rankings datasets and controlled for US county level characteristics in the following six domains: health outcomes, health behaviours, clinical care, social and economic factors, physical environment, and demographics.We also did a restricted cubic spline analysis to characterise the morphology of a non-linear association between PMand the risk of diabetes;assessed exposure in quartiles; assessed exposure in time-varying models, where geographical location was updated as participants moved and average annual exposure was matched to geographical location at any specific time; used the National Aeronautics and Space Administration's (NASA) Socioeconomic Data and Applications Center's Global Annual PMGrids from Moderate Resolution Imaging Spectroradiometer Multi-angle Imaging Spectroradiometer , and Sea-Viewing Wide Field-of View Sensor's aerosol optical depth remote spaceborne satellite sensing dataas an alternative data source for exposure; varied the spatial resolution of exposure definition where we assigned exposure levels on the basis of the nearest air monitoring station within 30 miles, 10 miles, and 5 miles; assessed the relationship between PMand risk of all-cause mortality as a positive control;assessed the relationship between ambient air sodium concentrations and risk of diabetes as a negative exposure control;assessed the relationship between ambient air sodium concentrations and risk of all-cause mortality; and assessed the relationship between PMand the risk of lower limb fracture. Further details on these sensitivity analyses are provided in the appendix (pp 5–11)

Cox proportional hazard models were used to examine the relationship between PMand the risk of diabetes, with censoring at death or end of follow-up. Selection of covariates was informed by previous studies.All models were adjusted for age, race, sex, estimated glomerular filtration rate, systolic blood pressure, hyperlipidaemia, chronic lung disease, cardiovascular disease, cancer, body-mass index, smoking status, use of an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, percentage of people in poverty in each county of residence, population density of county of residence, number of admissions to hospital before beginning of follow-up, and how many times serum creatinine was measured before beginning of follow-up. Further details on data sources, variable definitions, and statistical analyses are included in the appendix (pp 2–11) . Missing data were not imputed. In analyses, a 95% CI of a hazard ratio (HR) that does not include unity was considered significant. In all analyses, p<0·05 was considered significant.

We did a longitudinal cohort study of the association of PMwith diabetes. A cohort of US veterans with no previous history of diabetes was built by linking the US Department of Veterans Affairs' databaseswith the US Environmental Protection Agency's (EPA) Community Multiscale Air Quality Modeling System of PMwhere time of cohort entry was set as date of last outpatient blood panel between Oct 1, 2003, and Sept 30, 2004. Further details on these datasets and cohort construction are provided in the appendix (pp 2–3) . Participants were followed up for a median duration of 8·5 years. The outcome of incident diabetes was defined by International Classification of Diseases-9 code, diabetes medication prescription, or an HbAmeasurement more than 6·4% (>46·4 mmol/mol); and participants were censored at death or end of follow-up (Sept 30, 2012). PMexposure value was assigned on the basis of county of residence at time of cohort entry.

Mapping of the geographical distribution of age-standardised DALYs across the world showed populations in Central America, north Africa and the Middle East, southern sub-Saharan Africa, south Asia, and several countries in southeast Asia exhibited high age-standardised DALYs ( figure 4B appendix pp 36–51 ). Canada, Greenland, several countries in central and eastern Europe as well as central Asia, Russia, and Australia and New Zealand had low estimates of age-standardised DALYs ( figure 4B appendix pp 36–51 ). Finally, our estimates suggest that in 2016 there were 206 105 (95% UI 153 408–259 119) global deaths from diabetes attributable to PMexposure.

Among the ten most populated countries, India had the highest DALYs (1625·8, 95% UI 1193·7–2104·8), followed by China (1251·5, 828·5–1753·3), and then Indonesia (400·0, 261·7–544·5), in 1000s ( table 3 ). DALYs per 100 000 population showed Indonesia as the highest with 155·1 DALYs (95% UI 101·5–211·1), followed by India with 123·4 (90·7–159·9), and then the USA with 108·5 (59·3–163·9). Age-standardised DALYs per 100 000 population showed Pakistan as the highest with an age-adjusted DALY rate of 221·7 (95% UI 159·0–291·6), followed by Indonesia with 189·4 (124·4–255·7), and then India with 165·5 (122·5–212·3; table 3 ).

Age-standardised burden of incident diabetes attributable to PM 2·5 per 100 000 population (A) and age-standardised DALYs due to incident diabetes attributable to PM 2·5 per 100 000 population (B)

Figure 4 Age-standardised burden of incident diabetes attributable to PM 2·5 per 100 000 population (A) and age-standardised DALYs due to incident diabetes attributable to PM 2·5 per 100 000 population (B)

In 2016, the global burden of incident diabetes attributable to PMwas, in 1000s, 3002·9 (95% UI 2208·6–3798·9). Globally, ABD per 100 000 population was 40·62 (95% UI 29·9–51·4), and age-standardised ABD per 100 000 population was 40·4 (29·7–51·1; table 2 ). Global diabetes DALYs attributable to long-term exposure to PMwere 8·2 million (95% UI 5·8–11·0), consisting of 4·1 million (2·4–6·2) YLD and 4·1 million (3·1–5·1) YLL. The 2016 global YLD, YLL, and DALYs of diabetes attributable to PMin 1000s, in rate per 100 000 population, and age-standardised rate per 100 000 population are reported in table 3 . Age-standardised DALY rates per 100 000 population increased as World Bank income classification decreased, and also as sociodemographic index decreased ( figure 3 ).

YLD, YLL, and DALYs of diabetes associated with PM 2·5 exposure globally and for the top ten most populous countries

Table 3 YLD, YLL, and DALYs of diabetes associated with PM 2·5 exposure globally and for the top ten most populous countries

Attributable burden of diabetes associated with PM 2·5 exposure globally and for the top ten most populous countries

Table 2 Attributable burden of diabetes associated with PM 2·5 exposure globally and for the top ten most populous countries

A summary table listing the studies used in the analysis of synthesising the integrated exposure response function is provided in the appendix (pp 19–28) . The integrated exposure response function showed that the risk of diabetes increased substantially for PMconcentrations above the lower bound of the TMREL of 2·4 μg/mthen exhibited a more moderate increase in risk at concentrations above 10 μg/m figure 2 ).

A histogram of the distribution of PM 2·5 exposure among the countries is presented in the background in grey. The red line is the mean estimated relative risk. The black lines are 95% uncertainty intervals.

We examined the association of PMand risk of all-cause mortality where a priori observations suggest that an association is expected (ie, the positive outcome control).Our results showed a significant association between PMconcentrations and the risk of death (HR 1·08, 95% CI 1·03–1·13; table 1 ). We tested the association between ambient air sodium concentrations and the risk of diabetes (ie, a negative exposure control); the results showed a non-significant association (HR 1·00, 95% CI 0·99–1·00; table 1 ). There was also no significant association between air sodium concentrations and the risk of all-cause mortality as a negative exposure control (HR 1·00, 95% CI 1·00–1·01) and no significant association between PMand risk of lower limb fracture as a negative outcome control (1·00, 0·91–1·09; table 1 ).

We additionally considered PMestimates derived from NASA's spaceborne satellite sensors as an alternative data source to define ambient PMexposure concentrations. Analyses considering these data yielded results consistent with those shown using exposure data obtained from the EPA ground-based air monitoring stations (HR 1·13, 95% CI 1·11–1·15; table 1 ). Results were consistent in models where exposure concentrations were assigned on the basis of the nearest air monitoring station within 30 miles, 10 miles, and 5 miles ( appendix p 18 ).

We examined the relationship of PMand the risk of incident diabetes in a longitudinal cohort of 1 729 108 participants followed up for a median of 8·5 years (IQR 8·1–8·8). The demographic and health characteristics of the cohort participants are detailed in the appendix (pp 16–17) . PMconcentrations obtained from EPA ranged from 5·0 μg/mto 22·1 μg/m. In models adjusted for individual-level sociodemographic and health characteristics, a 10·0 μg/mincrease in PMexposure was associated with increased risk of diabetes (HR 1·15, 95% CI 1·08–1·22; table 1 ). Because characteristics of geographies might confound the association between PMand the risk of diabetes,we curated the US County Health Rankings' datasetsand built analyses additionally controlling for 55 US county-level variables in the six domains aforementioned. Models additionally adjusting for US county characteristics yielded consistent results in that an increase in PMwas associated with increased risk of diabetes (HR 1·12, 95% CI 1·02–1·24; table 1 ). A spline analysis suggested that the relationship between PMconcentrations and the risk of incident diabetes increased with increased concentrations of PMand then nearly plateaued at concentrations exceeding 12·0 μg/m figure 1 ). The results were consistent in analysis considering PMin quartiles; in that compared with quartile 1 (5·0–10·1 μg/m), the risk was increased in quartile 2 (consisting of PMconcentrations of 10·2–11·8 μg/m; HR 1·08, 95% CI 1·05–1·12) and then nearly plateaued in quartiles 3 and 4 (consisting of PMconcentrations ≥11·9 μg/m; HR 1·13 [95% CI 1·07–1·18] for quartile 3, and 1·14 [1·10–1·19] for quartile 4; table 1 ). Results were consistent when exposure was treated as time varying (HR 1·18, 95% CI 1·10–1·25), where it was updated as cohort participants moved from one location to another and as PMestimates changed over the duration of follow-up ( table 1 ).

The red line is the hazard ratio. The black lines are the 95% CIs. A histogram of the distribution of PM 2·5 exposure is presented in the background in grey. The lowest PM 2·5 value included in the analysis was 6·2 μg/m 3 and it served as the reference.

Discussion

Our results suggest that there is a significant association between increased PM 2·5 exposure and the risk of diabetes. Additionally, our integrated exposure response function suggests that risk is significant at concentrations below those recommended by regulatory agencies. Finally, we observed substantial geographical variation in the burden of diabetes attributable to air pollution, for which we estimated that in 2016, there were about 3·2 million cases of incident diabetes and about 8·2 million years of healthy life lost due to diabetes attributable to elevated concentrations of PM 2·5 .

2·5 pollution and the risk of diabetes is remarkably consistent across a number of studies from different populations; it is consistent when using EPA or NASA data to define exposure, and it passed the scrutiny of application of both positive and negative controls. The application of negative exposure and outcome controls is especially important to identify non-causal associations and serves as an important complement to other epidemiological methods for improving causal inference. 34 Lipsitch M

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Brook RD Air pollution and type 2 diabetes: mechanistic insights. The association of PMpollution and the risk of diabetes is remarkably consistent across a number of studies from different populations; it is consistent when using EPA or NASA data to define exposure, and it passed the scrutiny of application of both positive and negative controls. The application of negative exposure and outcome controls is especially important to identify non-causal associations and serves as an important complement to other epidemiological methods for improving causal inference.The biological mechanism underpinning the association is based on the premise that pollutants enter the bloodstream where they might interact with tissue components to produce pathological effects. This mechanism is now supported by evidence both in experimental models and humans that inhaled nanoparticles, which when sufficiently small can enter the bloodstream and interact with distant organs—including liver tissue—and exhibit affinity to accumulate at sites of vascular inflammation.Furthermore, experimental and human evidence suggests that exposure to ambient air pollutants can lead to clinically significant disturbances in the autonomic nervous system, oxidative stress, inflammation, endoplasmic reticulum stress, apoptosis, and broad metabolic derangements in glucose and insulin homoeostasis including glucose intolerance, decreased insulin sensitivity and impaired secretion, and increased blood lipid concentrations, thus providing biological mechanistic plausibility to the association of PMexposure and the risk of diabetes.

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Burnett R

et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. 2·5 will yield small reduction in risk; however, given the large populations living in heavily polluted geographies, even small incremental reductions in PM 2·5 will yield substantial reduction in the burden of diabetes. Our integrated exposure response function suggests that the risk of diabetes increased substantially between the TMREL and the air quality standards recommended by WHO (10 μg/m) and the EPA (12 μg/m); there was a more moderate increase in the risk at PMconcentrations greater than 10 μg/m. The findings are consistent with recent datasuggesting that even low concentration of air pollution might be unsafe, in which the unfortunate effect of air pollution becomes obvious at relatively low concentrations below those currently considered safe by regulatory agencies. The morphology of our integrated exposure response function is congruent with observations from other studies that examined the effect of PMand other non-communicable diseases,in which following an initial sharp increase the risk also nearly plateaued and subsequently exhibited minimal increase in risk.The non-linear integrated exposure response function implies that in the most polluted countries a modest reduction in PMwill yield small reduction in risk; however, given the large populations living in heavily polluted geographies, even small incremental reductions in PMwill yield substantial reduction in the burden of diabetes.

The toll of diabetes attributable to PM 2·5 pollution is substantial; long-term exposure to PM 2·5 contributed to about 3·2 million cases of diabetes in 2016, representing 14% of total incident diabetes globally. It contributed to about 8·2 million DALYs representing 14·4% of DALYs due to diabetes and 0·3% of the overall global toll of DALYs due to all diseases. The high toll is driven in part by the fact that more people breathe polluted air than ever before, as average population-weighted PM 2·5 exposure has increased by 11·2% from 39·7 μg/m3 in 1990 to 44·2 μg/m3 in 2015. Estimates of PM 2·5 attributable diabetes at the global and national levels reflect the influence not only of the increase in population-weighted PM 2·5 exposure, but also of demographic expansion and underlying epidemiological trends of increased burden of non-communicable disease in general, and more specifically diabetes.

2·5 pollution is not insignificant in well developed countries and in geographies with relatively lower air pollution. Developing a better understanding of the effect of low concentrations of pollution (those currently considered safe) on health should be also be addressed by funding agencies and the scientific community. 88 Di Q

Wang Y

Zanobetti A

et al. Air pollution and mortality in the Medicare population. Our results suggest substantial geographical variation in the burden of diabetes attributable to air pollution and that the burden is more heavily skewed toward regions that are less developed (low-income and lower-to-middle-income countries, and countries with a lower sociodemographic index). As countries develop economically and undergo an epidemiological transition, non-communicable diseases are likely to become even more prominent as major causes of disease and death, and the contribution of air pollution to non-communicable diseases in general, and specifically to diabetes will probably become even more pronounced. The forces of demographic expansion, ageing, epidemiological transition, and rapid industrialisation in low-income and lower-to-middle-income countries will probably increase the burden of health loss and death due to air pollution. The burden of health loss from diabetes attributable to PMpollution is not insignificant in well developed countries and in geographies with relatively lower air pollution. Developing a better understanding of the effect of low concentrations of pollution (those currently considered safe) on health should be also be addressed by funding agencies and the scientific community.Scientific evidence to define concentrations of particulate matter that are safe is needed to inform advocacy and guide policy making.

2·5 nor the chemical composition and toxic content of PM 2·5 , which might vary within and among countries; however, studies have shown that estimates using non-specific PM 2·5 biomass alone will underestimate the burden of disease attributable to PM 2·5 pollution. 1 Lelieveld J

Evans JS

Fnais M

Giannadaki D

Pozzer A The contribution of outdoor air pollution sources to premature mortality on a global scale. , 4 Cohen AJ

Brauer M

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et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. , 65 Brauer M

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et al. Ambient air pollution exposure estimation for the global burden of disease 2013. 2·5 exposure (ie, the Lancet Commission on pollution and health research recommendation number two); however, evaluation of the burden of diabetes associated with exposure to other pollutants including carbon monoxide, nitrogen dioxide, and others should be undertaken in future research. 86 Yang BY

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et al. Ambient air pollution in relation to diabetes and glucose-homoeostasis markers in China: a cross-sectional study with findings from the 33 Communities Chinese Health Study. , 89 Meo SA

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et al. Effect of environmental air pollution on type 2 diabetes mellitus. 90 Gill JM

Cooper AR Physical activity and prevention of type 2 diabetes mellitus. , 91 Sieverdes JC

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et al. Physical activity, cardiorespiratory fitness and the incidence of type 2 diabetes in a prospective study of men. , 92 Jefferis BJ

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Wannamethee SG Longitudinal associations between changes in physical activity and onset of type 2 diabetes in older British men: the influence of adiposity. 2·5 exposure; however, the successful application of both a negative exposure control and negative outcome control lessens the concern about residual confounding. Our analyses do not provide insight into the subnational level; this level is particularly important because several countries are especially large and there is likely to be substantial national geographical variation in both PM 2·5 and underlying morbidity and mortality rates related to diabetes (eg, in India and China) that is not captured in our analyses. In this study, we used estimates for incident diabetes generated by the GBD study group, and although these Bayesian estimates are considered robust, they are limited by the quality of the available data. 93 Thomas B

Matsushita K

Abate KH

et al. Global cardiovascular and renal outcomes of reduced GFR. 93 Thomas B

Matsushita K

Abate KH

et al. Global cardiovascular and renal outcomes of reduced GFR. 2·5 and the risk of diabetes was primarily derived from studies done in countries with relatively lower PM 2·5 air pollution (eg, USA, Canada, and western Europe), we relied on active and passive smoking as proxies for exposure to higher concentrations of PM 2·5 pollution to build our integrated exposure response function; 35 Burnett RT

Pope 3rd, CA

Ezzati M

et al. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. 2·5 exposure given the available data. 1 Lelieveld J

Evans JS

Fnais M

Giannadaki D

Pozzer A The contribution of outdoor air pollution sources to premature mortality on a global scale. , 4 Cohen AJ

Brauer M

Burnett R

et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. , 35 Burnett RT

Pope 3rd, CA

Ezzati M

et al. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. , 65 Brauer M

Freedman G

Frostad J

et al. Ambient air pollution exposure estimation for the global burden of disease 2013. , 94 Zhang Q

Jiang X

Tong D

et al. Transboundary health impacts of transported global air pollution and international trade. This study has several limitations. Our analyses neither considered the source of PMnor the chemical composition and toxic content of PM, which might vary within and among countries; however, studies have shown that estimates using non-specific PMbiomass alone will underestimate the burden of disease attributable to PMpollution.Our study focused on quantitating the burden of diabetes associated with PMexposure (ie, the Lancet Commission on pollution and health research recommendation number two); however, evaluation of the burden of diabetes associated with exposure to other pollutants including carbon monoxide, nitrogen dioxide, and others should be undertaken in future research.Although we accounted for several individual-level and county-level health characteristics, used two different data sources to define exposure, and took care to vary the spatial resolution of exposure definition, our analyses do not account for individual-level differences in socioeconomic status, physical activity,and PMexposure; however, the successful application of both a negative exposure control and negative outcome control lessens the concern about residual confounding. Our analyses do not provide insight into the subnational level; this level is particularly important because several countries are especially large and there is likely to be substantial national geographical variation in both PMand underlying morbidity and mortality rates related to diabetes (eg, in India and China) that is not captured in our analyses. In this study, we used estimates for incident diabetes generated by the GBD study group, and although these Bayesian estimates are considered robust, they are limited by the quality of the available data.Furthermore, variability and inconsistency of data collection methods and tools across the countries could influence geographical variations.Because data for the relationship of PMand the risk of diabetes was primarily derived from studies done in countries with relatively lower PMair pollution (eg, USA, Canada, and western Europe), we relied on active and passive smoking as proxies for exposure to higher concentrations of PMpollution to build our integrated exposure response function;this analytical strategy is well accepted, widely used, and represents the optimal methodological approach to quantitate the risk of disease associated with PMexposure given the available data.

Our study also had key strengths, such as the examination of the relationship between PM 2·5 and the risk of diabetes in a longitudinal cohort for which we also tested a positive control, negative exposure control, and negative outcome control to resolve concerns about causal inference. We also leveraged the availability of data from GBD 2016, which is the most comprehensive compilation and analysis of global health information available. We use GBD methodologies including the concept of DALYs to comprehensively capture the burden of disease across the world and a measure of uncertainty.