Acute Effect of Particulate Matter Pollution on Hospital Admissions for Stroke among Patients with type 2 Diabetes in Beijing, China, from 2014 to 2018

Acute Effect of Particulate Matter Pollution on Hospital Admissions for Stroke among Patients with type 2 Diabetes in Beijing, China, from 2014 to 2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Acute Effect of Particulate Matter Pollution on Hospital Admissions for Stroke among Patients with type 2 Diabetes in Beijing, China, from 2014 to 2018 Xiangtong Liu, Zhiwei Li, Moning Guo, Jie Zhang, Lixin Tao, Xiaolin Xu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-137286/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The health effect of particulate matter pollution on stroke has been widely examined; however, the effect among patients with comorbid type 2 diabetes (T2D) in developing countries has remained largely unknown. Methods : A time-series study was conducted to investigate the short-term effect of fine particulate matter (PM 2.5 ) and inhalable particulate matter (PM 10 ) on hospital admissions for stroke among patients with T2D in Beijing, China, from 2014 to 2018. An over-dispersed Poisson generalized additive model was employed to adjust for important covariates, such as weather conditions and long-term and seasonal trends. Results : A total of 159,298 (58% male) hospital admissions for stroke were reported. Linear exposure-response curves were observed for PM 2.5 and PM 10 in relation to stroke admissions among T2D patients. A 10 μg/m 3 increase in the four-day moving average of PM 2.5 and PM 10 was associated with 0.14% (95% confidence interval [CI]: 0.05%-0.23%) and 0.14% (95% CI: 0.06%-0.22%) incremental increases in stroke admissions among T2D patients, respectively. A 10 μg/m 3 increase in PM 2.5 in the two-day moving average corresponded to a 0.72% (95% CI: 0.02%-1.42%) incremental increase in hemorrhagic stroke, and a 10 μg/m 3 increase in PM 10 in the four-day moving average corresponded to a 0.14% (95% CI: 0.06%-0.22%) incremental increase in ischemic stroke. Conclusions : High particulate matter might be a risk factor for stroke among patients with T2D. PM 2.5 and PM 10 have a linear exposure-response relationship with stroke among T2D patients. The study provided evidence of the risk of comorbid T2D and stroke due to particulate matter pollution. Environmental Engineering Environmental Policy Particulate matter Stroke Type 2 diabetes Comorbidities Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights ♦ The health effect of particulate matter pollution on stroke among the patients with comorbid type 2 diabetes (T2D) in developing countries has remained largely unknown. ♦ Exposure to particulate matter is associated an increased risk of stroke admissions among the patient with comorbid T2D. ♦ Linear exposure-response curves were observed between PM 5 and PM 10 with stroke admissions among T2D patients. ♦ The study provided evidence of the risk of comorbid T2D and stroke due to particulate matter pollution. 1. Introduction Diabetes mellitus is the ninth major cause of death, and just under half a billion people worldwide were living with diabetes mellitus in 2019, 90% of whom had type 2 diabetes (T2D) (Saeedi et al., 2019a). Most patients with T2D have at least one complication, and stroke is the leading cause of mortality among these patients. China bears the largest stroke burden in the world, which has increased over the past 3 decades (Wang et al., 2017b), accounting for the leading cause of death and disability-adjusted life-years (Zhou et al., 2019). Over one-third of stroke patients have comorbid diabetes, leading to a poor prognosis (Lau et al., 2019). The morbidity of stroke was associated with the progression to diabetes (Xu et al., 2018). To reduce the prevalence of comorbid stroke and diabetes, it is vital to examine modifiable risk factors to formulate appropriate measures for prevention and control. Air pollutants are common modifiable risk factors for both stroke and T2D (Huang et al., 2019; Paul et al., 2020). Several studies have indicated that ambient air pollution is an important determinant of the risk of stroke (Fisher et al., 2019). Recently, the effect of air pollution on diabetes has attracted increasing attention (Liang et al., 2019; Yang et al., 2018b). Globally, 3.2 million incident cases of diabetes were due to ambient fine particulate matter, leading to 8.2 million disability adjusted life years (Bowe et al., 2018). However, the adverse effects of air pollution on the morbidity of stroke among people with comorbidities in the real world have remained largely unknown. Recently, Xue and colleagues conducted a case-crossover analysis of the China National Stroke Screening Survey and found that the first-ever stroke susceptibility associated with air pollution was significantly varied among Chinese adults (Xue et al., 2019). Li , et al. (Li et al., 2019) reported that people with cardiometabolic comorbidities, especially diabetes, were more vulnerable to air pollution. Epidemiological studies suggest that participants with T2D have lower vascular reactivity and thus may be more susceptible to cardiovascular effects related to air pollution (O'Donnell et al., 2011; O'Neill et al., 2005; Villeneuve et al., 2012). However, evidence of the association between air pollution and of the risk of stroke among T2D patients is still lacking in China. The burden of comorbidities has become a serious public health problem in many large cities, especially Beijing. However, the acute effects of ambient particulate matter exposure on stroke subtype admissions among T2D participants remain unknown in China. In this study, we aimed to examine the associations between ambient particulate matter (fine particulate matter with an aerodynamic diameter < 2.5 µm [PM 2.5 ] and inhalable particulate matter with an aerodynamic diameter < 10 µm [PM 10 ]) and total and subtype stroke admissions among patients with T2D in Beijing. In addition, differences in sex and age groups were tested by stratified analyses. 2. Materials And Methods Beijing, located in northern China, had a population of approximately 21.54 million in 2019. The total area of Beijing is 16, 410 square kilometers. Beijing has a rather dry, continental monsoon climate, with four distinct seasons including windy, cold winters and rainy, hot summers. 2.1 Hospital admission data Stoke admission records of patients with T2D were collected from the Beijing Municipal Health Commission Information Center (BMHCIC) ( http://www.phic.org.cn/ ) between Jan 1st, 2014, and Dec 31st, 2018. As a government agency, BMHCIC administers governmental hospitals, covering approximately 95% of medical services for permanent residents; it should be highly representative of Beijing permanent residents. The geographic locations of the 258 hospitals included in this study have been described elsewhere (Li et al., 2018). The data recording system has shown high validity in our previous study (Aklilu et al., 2020). Patient data captured from the medical record system included date of admission, T2D status, sex, age, principal diagnosis and secondary diagnosis on discharge. A history of T2D was coded as E12 based on the secondary diagnosis according to the International Classification of Diseases 10th Revision (ICD-10) codes and was defined as having fasting plasma glucose ≥ 7.1 mmol/L and/or current treatment of diabetes with antidiabetic medication before admission. The subtypes of the stroke hospitalizations were classified as hemorrhagic stroke (ICD-10: I60-I62, HS) and ischemic stroke (ICD-10: I63, IS). In the present study, total stroke admissions were calculated as the sum of HS and IS. Only stroke admissions among residents living in Beijing were included in the analysis (Figure S1). 2.2 Air pollutants and meteorological conditions data Daily concentrations of air pollutants including PM 2.5 , PM 10 , nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), ozone (O 3 ), and carbon monoxide (CO) in Beijing were retrieved from the Beijing Environmental Protection Bureau ( http://www.bjepb.gov.cn/ ) from Jan 1st, 2014, to Dec 31st, 2018. The average concentration of air pollutant data collected was based on 35 ambient air quality monitoring stations. These stations are distributed in 16 districts of Beijing city. The geographic locations of the 35 monitoring stations included in this study have been described elsewhere and have been proven to have good reliability and validity (Huang et al., 2015). Values for the daily mean temperature and relative humidity data used in the model to adjust for confounders were obtained from the China Meteorological Data Sharing Service System online ( https://data.cma.cn/en ) over the same time period. 2.3 Ethical clearance The study was approved by the Institutional Review Board of Capital Medical University (No. IRB00009511). Informed consent was not specifically required since personal identifiers were not collected. 2.4 Statistical analysis As the daily counts of hospital admissions generally followed a Poisson distribution (Yu et al., 2019), we applied an over-dispersed generalized additive model (GAM) to examine the short-term effect of particulate matter on stroke admissions among people with T2D (Song et al., 2018). Specifically, covariates were included in the main model as follows: (1) natural smooth functions of temperature and relative humidity with 3 degrees of freedom ( df ) to control for the nonlinear confounding effects of weather factors (Wang et al., 2017a); (2) indicator variables for public holidays and day of week; and (3) a natural cubic spline function with 7 df for calendar time to exclude unmeasured long-term and seasonal trends of daily stroke admissions (Chen et al., 2018b). The exposure-response curves were plotted between particulate matter variables and daily stroke admissions of patients with T2D. According to a previous study (Chen et al., 2018a), a natural spline function of 3 df was added into the model. According to the Akaike information criterion, generalized cross validation, and partial autocorrelation function, multiple lag structures including single-day lags (from 0 to 7) and moving average exposure of multiple days (lag 0–1, 0–2, 0–3, 0–4, 0–5, 0–6, 0–7) were used to determine the best lag structure for estimation. Sensitivity analyses were conducted to examine the robustness of the effect. First, we set the df value from 4 to 10 per year. Second, principal component analysis was applied to cope with collinearity among air pollutants to estimate the independent effects of particulate matter (Feng et al., 2019) (Stafoggia et al., 2017). For instance, to estimate the independent effects of PM 2.5 , we first substituted all primary air pollutants (PM 10 , NO 2 , SO 2 , O 3 , and CO) with a composite latent variable (a linear combination of all air pollutants) through principal component analysis, and then included the latent variable in the conditional GAM model. Then the coefficient β′ of the latent variable was transformed into the coefficient β of the original air pollutants. The data were also classified according to sex, age, subtype and T2D comorbid conditions. Patients were classified into two age groups: younger (18–64 years) and elderly (≥ 65 years). Z tests were used to compare differences between effect estimates in the strata (Lin et al., 2016). The statistical tests were two-sided, and P < 0.05 was considered statistically significant. All statistical analyses were conducted in R software (version 4.0.2) using the MGCV package. The effects are described as the percent changes and 95% CI in daily count on admissions for stroke per 10 µg/m 3 increase in PM 2.5 and PM 10 among patients with T2D. 3. Results A total of 159,298 admissions for stroke were extracted from BMHCIC between 2014 and 2018; after excluding nonlocal admissions, 149,757 (94%) hospitalizations for ischemic stroke were included. Male and elderly (≥ 65 years) participants accounted for 58% and 61%, respectively. Table 1 summarizes the characteristics and distribution of daily stroke admissions, stratified by subtype, sex, and age. The distribution of air pollutants, meteorological factors, and the counts of daily admissions for stroke across four seasons from 2014 to 2018 are described in Fig. 1 . The annual average values of daily mean concentrations of PM 2.5 and PM 10 were 72.1 µg/m 3 and 104.5 µg/m 3 , respectively. The concentrations of PM 2.5 and PM 10 were 1.5 and 2.1 times the Chinese Ambient Air Quality Standards limit, respectively. The median temperature was 14.4 °C, and the average relative humidity was 54.3 ± 19.2%. Air pollutants except O 3 were strongly correlated with each other (coefficients distributed from 0.49 to 0.87) and moderately correlated with temperature and relative humidity (coefficients distributed from − 0.41 to 0.33) (Fig. 2 ). Table 1 Distribution characteristics of daily stroke events among T2D patients ( n = 159, 298) Overall stroke Ischemic stroke Hemorrhagic stroke No. (%) P 25 -P 75 No. (%) P 25 -P 75 No. (%) P 25 -P 75 Total 159298 (100) 56–110 149757 (94) 52–104 9541 (6) 3–7 Sex Male 92977 (58) 33–65 87285 (58) 30–61 5692 (60) 2–4 Female 66317 (42) 24–46 62469 (42) 23–44 3848 (40) 1–3 Age (years) 18–64 61672 (39) 23–43 57454 (38) 21–40 4218 (44) 1–3 ≥ 65 97626 (61) 34–68 92303 (62) 31–65 5323 (56) 2–4 Note: No. (%) -counts of event and percentage, P 25 –P 75 - 25th percentile and 75th percentile. Table 2 and Table 3 show the effect estimates of PM 2.5 and PM 10 concentrations on daily stroke admissions among T2D patients according to subtype, sex, and age. Overall, PM 2.5 and PM 10 were significantly associated with hospitalizations for stroke among T2D patients. According to the smallest parameter value, lags of 4 days (lag4) were determined to be the best lag structure for both PM 2.5 and PM 10 . A 10 µg/m 3 increase in PM 2.5 (lag4) and PM 10 (lag4) corresponded to 0.14% (95% CI: 0.05%-0.23%) and 0.14% (95% CI: 0.06%-0.22%) incremental increased in stroke admissions among diabetic patients, respectively. Table 2 Percentage changes (95% CI) in stroke admissions per 10 ug/m 3 increase in PM 2.5 among T2D patients grouping by sex, age, and subtype. Lag days Total Ischemic stroke Hemorrhagic stroke Men Women Age 18–64 y Age ≥ 65 y Lag 0 days 0.18 (-0.02, 0.38) 0.28 (-0.90, 1.48) 0.47 (-0.36, 1.31) 0.18 (-0.08, 0.45) 0.18 (-0.14, 0.49) 0.39 (0.06, 0.71) 0.06 (-0.20, 0.31) Lag 1 days 0.01 (-0.10, 0.11) -0.19 (-0.55, 0.17) -0.32 (-0.74, 0.11) 0.04 (-0.10, 0.17) -0.03 (-0.20, 0.13) 0.10 (-0.07, 0.27) -0.05 (-0.19, 0.08) Lag 2 days 0.01 (-0.08, 0.10) -0.09 (-0.40, 0.22) -0.14 (-0.51, 0.22) -0.03 (-0.15, 0.09) 0.06 (-0.08, 0.20) 0.06 (-0.08, 0.21) -0.02 (-0.14, 0.09) Lag 3 days 0.04 (-0.05, 0.13) -0.28 (-0.59, 0.04) -0.35 (-0.72, 0.01) 0.01 (-0.11, 0.13) 0.08 (-0.06, 0.22) 0.12 (-0.02, 0.27) -0.01 (-0.13, 0.11) Lag 4 days 0.14 (0.05, 0.23) 0.12 (-0.19, 0.43) 0.10 (-0.26, 0.46) 0.15 (0.03, 0.27) 0.11 (-0.03, 0.25) 0.21 (0.06, 0.35) 0.09 (-0.03, 0.21) Lag 5 days 0.10 (0.01, 0.19) 0.26 (-0.04, 0.56) 0.24 (-0.11, 0.59) 0.09 (-0.03, 0.20) 0.12 (-0.02, 0.25) 0.12 (-0.02, 0.26) 0.09 (-0.03, 0.20) Lag 6 days 0.11 (0.02, 0.19) 0.05 (-0.25, 0.35) 0.16 (-0.19, 0.51) 0.09 (-0.02, 0.21) 0.13 (-0.01, 0.26) 0.14 (0.00, 0.28) 0.08 (-0.03, 0.20) Lag 7 days 0.07 (-0.02, 0.16) 0.19 (-0.11, 0.49) 0.26 (-0.09, 0.62) 0.07 (-0.04, 0.19) 0.06 (-0.07, 0.20) 0.11 (-0.03, 0.25) 0.04 (-0.07, 0.16) Lag 0–1 days 0.10 (-0.07, 0.27) 0.62 (-0.04, 1.29) 0.72 (0.02, 1.42) 0.12 (-0.11, 0.34) 0.08 (-0.18, 0.35) 0.09 (-0.18, 0.37) 0.11 (-0.11, 0.33) Lag 0–2 days 0.09 (-0.10, 0.29) 0.28 (-0.48, 1.04) 0.27 (-0.51, 1.07) 0.13 (-0.13, 0.38) 0.04 (-0.26, 0.34) 0.18 (-0.13, 0.49) 0.04 (-0.21, 0.29) Lag 0–3 days 0.02 (-0.16, 0.21) 0.31 (-0.37, 0.99) 0.46 (-0.29, 1.22) 0.06 (-0.19, 0.30) -0.02 (-0.31, 0.26) 0.10 (-0.19, 0.40) -0.03 (-0.26, 0.21) Lag 0–4 days 0.06 (-0.13, 0.26) 0.26 (-0.43, 0.95) 0.35 (-0.43, 1.14) 0.07 (-0.19, 0.32) 0.06 (-0.24, 0.36) 0.18 (-0.14, 0.49) 0.00 (-0.25, 0.24) Lag 0–5 days -0.02 (-0.22, 0.18) 0.51 (-0.18, 1.21) 0.51 (-0.29, 1.32) -0.07 (-0.33, 0.19) 0.05 (-0.25, 0.36) 0.10 (-0.22, 0.42) -0.09 (-0.34, 0.17) Lag 0–6 days 0.03 (-0.18, 0.24) 0.34 (-0.38, 1.07) 0.28 (-0.56, 1.13) -0.05 (-0.32, 0.23) 0.14 (-0.18, 0.46) 0.21 (-0.13, 0.54) -0.08 (-0.35, 0.19) Lag 0–7 days -0.03 (-0.25, 0.19) 0.43 (-0.33, 1.19) 0.28 (-0.60, 1.17) -0.11 (-0.39, 0.18) 0.07 (-0.27, 0.41) 0.06 (-0.30, 0.41) -0.09 (-0.37, 0.19) * Statistically positive significant results at the 5% level ( P < 0.05) are indicated in bold. T2D: type 2 diabetes. Table 3 Percentage changes (95% CI) in stroke admissions per 10 ug/m 3 increase in PM 10 among T2D patients grouping by sex, age, and subtype. Lag days Total Ischemic stroke Hemorrhagic stroke Men Women Age 18–64 y Age ≥ 65 y Lag 0 days 0.17 (-0.12, 0.46) 0.16 (-0.13, 0.46) 0.28 (-0.90, 1.48) 0.25 (-0.12, 0.63) 0.06 (-0.39, 0.50) 0.23 (-0.24, 0.69) 0.14 (-0.23, 0.50) Lag 1 days 0.03 (-0.05, 0.12) 0.05 (-0.04, 0.14) -0.19 (-0.55, 0.17) 0.05 (-0.07, 0.16) 0.02 (-0.12, 0.15) 0.13 (-0.01, 0.27) -0.02 (-0.14, 0.09) Lag 2 days 0.02 (-0.06, 0.09) 0.02 (-0.05, 0.10) -0.09 (-0.40, 0.22) -0.01 (-0.11, 0.09) 0.06 (-0.06, 0.18) 0.07 (-0.05, 0.20) -0.02 (-0.12, 0.08) Lag 3 days 0.06 (-0.02, 0.14) 0.08 (0.00, 0.17) -0.28 (-0.59, 0.04) 0.04 (-0.06, 0.14) 0.09 (-0.03, 0.21) 0.15 (0.03, 0.28) 0.00 (-0.10, 0.10) Lag 4 days 0.14 (0.06, 0.22) 0.14 (0.06, 0.22) 0.12 (-0.19, 0.43) 0.14 (0.04, 0.24) 0.14 (0.02, 0.26) 0.20 (0.08, 0.33) 0.10 (0.00, 0.20) Lag 5 days 0.12 (0.04, 0.19) 0.11 (0.03, 0.18) 0.26 (-0.04, 0.56) 0.09 (-0.01, 0.19) 0.15 (0.04, 0.27) 0.13 (0.01, 0.25) 0.11 (0.01, 0.20) Lag 6 days 0.11 (0.04, 0.18) 0.11 (0.04, 0.19) 0.05 (-0.25, 0.35) 0.08 (-0.01, 0.18) 0.15 (0.03, 0.26) 0.10 (-0.02, 0.22) 0.12 (0.02, 0.21) Lag 7 days 0.08 (0.00, 0.16) 0.07 (-0.01, 0.15) 0.19 (-0.11, 0.49) 0.10 (0.00, 0.20) 0.06 (-0.06, 0.17) 0.08 (-0.04, 0.20) 0.08 (-0.01, 0.18) Lag 0–1 days 0.14 (-0.03, 0.30) 0.11 (-0.06, 0.27) 0.62 (-0.04, 1.29) 0.16 (-0.05, 0.38) 0.10 (-0.16, 0.35) 0.08 (-0.18, 0.35) 0.17 (-0.04, 0.38) Lag 0–2 days 0.15 (-0.03, 0.34) 0.15 (-0.05, 0.34) 0.28 (-0.48, 1.04) 0.18 (-0.07, 0.43) 0.12 (-0.17, 0.41) 0.24 (-0.06, 0.54) 0.10 (-0.14, 0.34) Lag 0–3 days 0.10 (-0.07, 0.26) 0.08 (-0.09, 0.26) 0.31 (-0.37, 0.99) 0.12 (-0.09, 0.34) 0.06 (-0.20, 0.32) 0.19 (-0.08, 0.46) 0.04 (-0.18, 0.25) Lag 0–4 days 0.11 (-0.06, 0.28) 0.10 (-0.08, 0.27) 0.26 (-0.43, 0.95) 0.09 (-0.13, 0.31) 0.13 (-0.14, 0.39) 0.23 (-0.04, 0.50) 0.03 (-0.19, 0.24) Lag 0–5 days 0.01 (-0.16, 0.18) -0.02 (-0.20, 0.15) 0.51 (-0.18, 1.21) -0.05 (-0.28, 0.17) 0.09 (-0.17, 0.36) 0.12 (-0.16, 0.40) -0.06 (-0.28, 0.16) Lag 0–6 days 0.04 (-0.14, 0.22) 0.02 (-0.16, 0.21) 0.34 (-0.38, 1.07) -0.05 (-0.29, 0.18) 0.17 (-0.10, 0.45) 0.21 (-0.08, 0.50) -0.06 (-0.29, 0.17) Lag 0–7 days -0.08 (-0.27, 0.11) -0.11 (-0.30, 0.09) 0.43(-0.33, 1.19) -0.16 (-0.40, 0.09) 0.04 (-0.26, 0.33) 0.03 (-0.27, 0.34) -0.15 (-0.39, 0.10) * Statistically positive significant results at the 5% level ( P < 0.05) are indicated in bold. Specifically, for PM 2.5 , a 10 µg/m 3 increase corresponded to a 0.72% (95% CI: 0.02%-1.42%) incremental increase in hemorrhagic stroke (lag 0–1). The incremental increase in hemorrhagic stroke was 0.15% (95% CI: 0.03%-0.27%) in men (lag 4), and 0.39% (95% CI: 0.06%-0.71%) in participants aged 18–64 years (lag 0) (Table 2 ). For PM 10 , a 10 µg/m 3 increase in the four-day moving average corresponded to a 0.14% (95% CI: 0.06%-0.22%) incremental increase in ischemic stroke. The effect size was 0.14% (95% CI: 0.04%-0.24%) for men and 0.15 (95% CI: 0.04%-0.27%) for women, and the difference was not statistically significant ( Z =-0.29, P = 0.77). The effect size in participants aged 18–64 years was significantly higher than that in participants aged ≥ 65 years [0.20% (95% CI: 0.08%-0.33%) vs. 0.12% (95% CI: 0.02%-0.21%), Z =-1.62, P = 0.10] (Table 3 ). Subgroup analysis indicated that the onset of the maximum effect of PM 10 was much earlier for males and younger adults than for females and elderly adults (Table 3 ). The exposure-response curves of the overall association between PM 2.5 and stroke admissions among T2D patients are shown in Fig. 3 . Monotonic increasing curves were observed overall and in each of the subgroups of T2D patients, which clearly indicated that the effect of PM 2.5 on stroke is linear. Evidently, high risks were observed for high PM 2.5 concentrations, with flat slopes at low concentrations and steeper slopes for concentrations ≥ 200 µg/m 3 . The exposure-response curves for the overall association between PM 10 and stroke admissions among T2D patients are shown in Fig. 4 . Monotonic increasing curves were observed overall and in subgroups of T2D patients, which indicated that the effect of PM 10 on stroke is almost linear. Evidently, high risks were observed for high PM 10 concentrations, with flat slopes at low concentrations and steeper slopes for concentrations ≥ 200 µg/m 3 . 4. Discussion To the best of our knowledge, this is the first study that attempted to determine associations between particulate matter pollution and stroke admissions among patients with T2D in China. Generally, this research showed that short-term exposure to particulate matter pollution was associated with an increased risk of stroke admissions among patients with T2D. The associations between particulate matter and stroke admissions in patients with T2D varied by sex, age and subtype, and the associations were robust when adjusted for copollutants. This study added to the limited evidence of acute effects of particulate matter pollution in populations with comorbidities in developing countries. Between 2014 and 2018, the average concentrations of PM 2.5 (72.1 µg/m 3 ) and PM 10 (104.5 µg/m 3 ) in Beijing far exceeded the Chinese National Ambient Air Quality Standards (35 µg/m 3 and 70 µg/m 3 , respectively). Meanwhile, T2D has become the leading risk factor for cerebrovascular disease in the Chinese population. It is estimated that 116.4 million Chinese adults had T2D in 2019, ranking first in the world (Saeedi et al., 2019b). Although the adverse effect attributable to PM exposure might be small in an individual, the overall attributable risk might be considerably higher, given the high pollutant concentrations and large population of T2D in China (Maji et al., 2017; Maji et al., 2018). The number of studies on the short-term effect of particulate matter on stroke has increased during the past few years. Significant associations were observed between particulate matter (PM 2.5 and PM 10 ) concentrations and admissions for stroke, which are roughly consistent with results from previous studies (Huang et al., 2016; Tian et al., 2017). Liu et al found that an interquartile range increase in PM 2.5 (47.5 µg/m 3 ) and PM 10 (76.9 µg/m 3 ) was significantly associated with admissions for ischemic stroke, with per centage changes of 1.0% (95% CI: 0.7%-1.4%) and 0.8% (95% CI: 0.3%-1.3%), respectively (Liu et al., 2017b). Previous research showed that short-term exposure to PM 2.5 and PM 10 was positively related to stroke admission and mortality (RRs: 1.011 (95% CI: 1.011–1.012) and 1.003 (95% CI: 1.002–1.004), respectively) (Shah et al., 2015). An interquartile range of PM 10 corresponded to 0.7% (95% CI: 0%-1.4%) increases in admissions for ischemic stroke (Liu et al., 2017a). We found that for patients with comorbid T2D, the effect estimates for both PM 2.5 and PM 10 using moving average lags were much higher than those using single day lags. Most previous studies were designed to study the effects of particulate matter on the morbidity or mortality of stroke. Our study suggests that for participants with T2D, more attention should be paid to the health hazards of air pollution. Similarly, Yang et al. (Yang et al., 2020) found that patients with T2D might be more vulnerable to air pollutant exposure, and Paul et al. found that exposure to PM was associated with an increased risk of cardiovascular diseases among populations with prevalent T2D (Paul et al., 2020). Even if the exact mechanisms behind the association of air pollution with increased admission in the T2D group have not been determined, there is growing evidence suggesting that the diabetes effect of PM pollution might be caused by endothelial dysfunction and systemic proinflammatory processes, leading to insulin resistance (Rajagopalan et al., 2018). Alterations in metabolic and inflammatory processes are crucial intermediate steps in explaining how PM exposure increases the risk of T2D, which is characterized by low-grade systemic inflammation (Lao et al., 2019). Previous research suggests that direct exposure of the respiratory tract to PM produces local inflammatory responses in lung tissue (Honda et al., 2017). It is hypothesized that upon chronic PM exposure, these local inflammatory processes spread to other organ systems and then lead to blood glucose metabolism abnormalities (Hotamisligil, 2017). A recent study indicated that adiponectin may be a potential mediator along the causal pathway (Lucht et al., 2020). These mechanisms might overlap at different times, and further studies are warranted to clarify them in detail. The effect size of PM 2.5 was much higher than that of PM 10 , which is generally consistent with existing studies (Huang et al., 2016; Zeng et al., 2018). Several mechanisms were proposed to explain the difference. First, PM 2.5 can penetrate the systemic circulation and then reach their way into the alveoli as well, whereas PM 10 can enter only the respiratory tract. Second, PM 2.5 has a higher particulate number and larger active surface area than PM 10 . Third, the larger surface area could carry a much larger volume of toxic pollutants to generate proinflammatory responses. Consistent with a previous study (Zeng et al., 2018), higher effect estimates were reported in younger adults (18–64 years). The reasons for the difference between age groups remain unknown. As air pollution has become an increasingly serious public health problem during the past few decades, and younger generations may suffer from air pollution earlier in their life cycle. Furthermore, older participants may pay more attention to preventing environmental hazards from PM pollution. For example, older participants are prone to stay at home during heavy pollution days. The present study showed the increased effect of air pollution exposure on stroke admissions among patients with T2D. The inhalation of toxic substances in individuals with compromised metabolic function due to diabetes may contribute to stroke by weakening the immune system and further decreasing metabolic function. In our study, the effect of particulate matter on admissions for overall stroke varied by sex among patients with T2D. Consistent with previous research, we found that PM 2.5 had stronger effects in male participants, whereas PM 10 had stronger effects in female participants. Hwang and colleagues demonstrated that the sex-specific effects could be explained by biological differences as well as differences in social exposures (Hwang et al., 2020). Generally, lung size, gas-blood barrier permeability and inflammation all vary by sex. Xia reported that, among surviving stroke patients, women were more susceptible to diabetes than men (Xia et al., 2019). Furthermore, men participate in more outdoor activities with fewer personal safeguard measures (e.g. wearing particulate-filtering masks), leading to increased exposure to particulate matter (Yang et al., 2018a). The determination of exposure-response relationships is critical for public health assessment of those with preexisting noncommunicable diseases. In our study, linear associations were observed between particulate matter and daily stroke admissions among patients with T2D. Consistent with a previous study, monotonically increasing curves were observed overall and in each of the subgroups of T2D patients which clearly suggests that the effect of both PM 2.5 and PM 10 on stroke is linear (Costa et al., 2017). Particulate matter concentrations should be further limited to protect people with T2D, although exposure-response relationships may differ by air pollution mixture, meteorological conditions, and population susceptibility (Song et al., 2019). The study had some limitations. First, we could not collect data on the relationships between patients. However, there might be a few family aggregation cases, which may affect the population sensitivity (Yang et al., 2018c). Second, we used fixed-site monitor measurements as a proxy for personal exposure, which may result in exposure errors and underestimation of the associations between ambient air pollution and diseases. Third, the generalizability of our results might be limited as the study collected data from only one highly polluted city. Conclusions This study suggests that short-term exposure to ambient PM 2.5 and PM 10 significantly increased the risk of stroke admissions among patients with T2D. Linear exposure-response curves were observed for PM 2.5 and PM 10 in relation to stroke admissions among T2D patients, with flat slopes at low concentrations and steeper slopes for high concentrations. High particulate matter concentrations might be one risk factor for stroke among patients with T2D in Beijing, China. The study provided evidence of the risk of comorbidities due to particulate matter pollution. Abbreviations T2D: type 2 diabetes; PM 2.5 : fine particulate matter with an aerodynamic diameter < 2.5 μm; PM 10 : inhalable particulate matter with an aerodynamic diameter <10 μm; 95% CI: 95% confidence interval; BMHCIC: the Beijing Municipal Health Commission Information Center; ICD-10: the International Classification of Diseases, 10 th Revision; HS: hemorrhagic stroke; IS: ischemic stroke; NO 2 : nitrogen dioxide; SO 2 : sulfur dioxide; O 3 : ozone; CO: carbon monoxide; GAM: generalized additive model; df : degrees of freedom; SD: standard deviation; IQR: interquartile range. Declarations Acknowledgments We acknowledge the Beijing Municipal Health Commission Information Center and the Beijing Air Pollution and Metrological Data Center. Funding This study was supported by National Natural Science Foundation of China (No. 82003559), Nature Science Foundation of Capital Medical University (No. PYZ2018046), Beijing Municipal Training Project of Excellent Talents. The funding was neither used for the study design nor data collection but to cover for the publication fees. Availability of data and materials The data can be accessed from the Beijing Municipal Health Commission Information Center with permission via direct request. Authors’ contributions Xiangtong Liu: Conceptualization, Methodology, Data curation, Writing- Original draft preparation, Funding acquisition; Zhiwei Li: Methodology, Software, Data curation, Visualization; Moning Guo: Data curation, Resources; Jie Zhang: Supervision, Data curation; Lixin Tao: Writing- Reviewing and Editing; Xiaolin Xu: Writing- Reviewing and Editing; Aklilu Deginet: Writing- Reviewing and Editing; Feng Lu: Resources; Yanxia Luo: Conceptualization, Methodology; Mengmeng Liu: Formal analysis; Mengyang Liu: Formal analysis; Yue Sun: Formal analysis; Haibin Li: Formal analysis; Xiuhua Guo: Supervision, Writing- Reviewing and Editing. All authors were involved with the critical revision of the manuscript and approved the final version. Ethics approval In this study, informed consent was not specifically required because we did not use personal data identifiers. However, the study was approved by the Institutional Review Board of Capital Medical University with the IRB00009511 identification number. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Aklilu, D., et al., 2020. Short-term effects of extreme temperatures on cause specific cardiovascular admissions in Beijing, China. Environ Res. 186 , 109455. Bowe, B., et al., 2018. The 2016 global and national burden of diabetes mellitus attributable to PM2.5 air pollution. Lancet Planet Health. 2 , e301-e312. Chen, C., et al., 2018a. Ambient air pollution and daily hospital admissions for mental disorders in Shanghai, China. Sci Total Environ. 613-614 , 324-330. Chen, F., et al., 2018b. 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Epidemiology. 22 , 422-31. O'Neill, M. S., et al., 2005. Diabetes enhances vulnerability to particulate air pollution-associated impairment in vascular reactivity and endothelial function. Circulation. 111 , 2913-20. Paul, L. A., et al., 2020. The impact of air pollution on the incidence of diabetes and survival among prevalent diabetes cases. Environ Int. 134 , 105333. Rajagopalan, S., et al., 2018. Air Pollution and Cardiovascular Disease: JACC State-of-the-Art Review. J Am Coll Cardiol. 72 , 2054-2070. Saeedi, P., et al., 2019a. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 157 , 107843. Saeedi, P., et al., 2019b. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9 th edition. Diabetes Res Clin Pract. 157 , 107843. Shah, A. S., et al., 2015. Short term exposure to air pollution and stroke: systematic review and meta-analysis. BMJ. 350 , h1295. Song, J., et al., 2019. Acute effect of ambient air pollution on hospitalization in patients with hypertension: A time-series study in Shijiazhuang, China. Ecotoxicol Environ Saf. 170 , 286-292. Song, J., et al., 2018. Acute effects of ambient particulate matter pollution on hospital admissions for mental and behavioral disorders: A time-series study in Shijiazhuang, China. Sci Total Environ. 636 , 205-211. Stafoggia, M., et al., 2017. Statistical Approaches to Address Multi-Pollutant Mixtures and Multiple Exposures: the State of the Science. Curr Environ Health Rep. 4 , 481-490. Tian, Y., et al., 2017. Fine Particulate Air Pollution and First Hospital Admissions for Ischemic Stroke in Beijing, China. Sci Rep. 7 , 3897. Villeneuve, P. J., et al., 2012. Short-term effects of ambient air pollution on stroke: who is most vulnerable? Sci Total Environ. 430 , 193-201. Wang, Q., et al., 2017a. Hypertension modifies the short-term effects of temperature on morbidity of hemorrhagic stroke. Sci Total Environ. 598 , 198-203. Wang, W., et al., 2017b. Prevalence, Incidence, and Mortality of Stroke in China: Results from a Nationwide Population-Based Survey of 480 687 Adults. Circulation. 135 , 759-771. Xia, X., et al., 2019. Prevalence and risk factors of stroke in the elderly in Northern China: data from the National Stroke Screening Survey. J Neurol. 266 , 1449-1458. Xu, X., et al., 2018. Progression of diabetes, heart disease, and stroke multimorbidity in middle-aged women: A 20-year cohort study. PLoS Med. 15 , e1002516. Xue, T., et al., 2019. A national case-crossover study on ambient ozone pollution and first-ever stroke among Chinese adults: Interpreting a weak association via differential susceptibility. Sci Total Environ. 654 , 135-143. Yang, B. Y., et al., 2020. Ambient air pollution and diabetes: A systematic review and meta-analysis. Environ Res. 180 , 108817. Yang, B. Y., et al., 2018a. Global association between ambient air pollution and blood pressure: A systematic review and meta-analysis. Environ Pollut. 235 , 576-588. Yang, B. Y., et al., 2018b. 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. Lancet Planet Health. 2 , e64-e73. Yang, Y., et al., 2018c. Ambient fine particulate pollution associated with diabetes mellitus among the elderly aged 50 years and older in China. Environ Pollut. 243 , 815-823. Yu, Y., et al., 2019. Association between short-term exposure to particulate matter air pollution and cause-specific mortality in Changzhou, China. Environ Res. 170 , 7-15. Zeng, W., et al., 2018. Ambient fine particulate pollution and daily morbidity of stroke in Chengdu, China. PLoS One. 13 , e0206836. Zhou, M., et al., 2019. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 394 , 1145-1158. Supplementary Files 4Supplementalfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-137286","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":7181890,"identity":"97f11f24-67ac-4b13-b8d8-d67b23ebf693","order_by":0,"name":"Xiangtong Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiangtong","middleName":"","lastName":"Liu","suffix":""},{"id":7181891,"identity":"efb8764c-a237-4e34-a41c-989fc43c58b5","order_by":1,"name":"Zhiwei Li","email":"","orcid":"","institution":"Capital Medical 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16:45:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-137286/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-137286/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4717260,"identity":"20c69e16-12d3-4d02-be9d-e7555c110b04","added_by":"auto","created_at":"2021-01-05 15:26:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53959,"visible":true,"origin":"","legend":"Boxplots of air pollutants, meteorological factors, and the daily counts of admissions for stroke during 2014 and 2018. (a) The seasonal pattern of air pollutants; (b) The seasonal pattern of meteorological conditions; (c) The seasonal pattern of daily admissions for stroke. Analysis of variance or Kruskal-Wallis tests were applied to examine the difference across four seasons. PM2.5: particles with an aerodynamic diameter ≤ 2.5 μm; PM10: particles with an aerodynamic diameter ≤ 10 μm; NO2: nitrogen dioxide; SO2: sulfur dioxide; O3: ozone; CO: carbon monoxide. ","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-137286/v1/183574f50139b79b7d629769.png"},{"id":4717413,"identity":"6f84d403-a304-4689-a6a0-189d4c65f672","added_by":"auto","created_at":"2021-01-05 15:29:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48839,"visible":true,"origin":"","legend":"Pearson correlation coefficients between air pollution concentrations and weather factors and daily admissions for stroke among T2D patients in Beijing, 2014–2018 (n = 1799). NO2: nitrogen dioxide; SO2: sulfur dioxide; O3: ozone; CO: carbon monoxide; PM2.5: particles with an aerodynamic diameter ≤ 2.5 μm; PM10: particles with an aerodynamic diameter ≤ 10 μm. * indicates 0.01\u003cP≤0.05; ** indicates 0.001\u003cP≤0.01; *** indicates P≤0.001. ","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-137286/v1/5a8fcb93c4660f98e3673870.png"},{"id":4717268,"identity":"427b3e5e-afb3-4a7b-a10b-8857c2387019","added_by":"auto","created_at":"2021-01-05 15:26:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71254,"visible":true,"origin":"","legend":"Dose-response relationship between PM2.5 (degrees of freedom = 3) and stroke admissions in Beijing, 2014-2018 using multi-pollutant model, after adjusted for temperature, relative humidity, day of week and public holidays.","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-137286/v1/6fbbd87e2a04222711cc948d.png"},{"id":4717272,"identity":"9378d346-0dae-446f-ba9a-048c12fe3064","added_by":"auto","created_at":"2021-01-05 15:26:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84451,"visible":true,"origin":"","legend":"Dose-response relationship between PM10 (degrees of freedom = 3) and stroke admissions in Beijing, 2014-2018 using multi-pollutant model, after adjusted for temperature, relative humidity, day of week and public holidays.","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-137286/v1/fd4678e9856f91f0c3aededa.png"},{"id":13643082,"identity":"ea826774-9962-4d10-ab06-d029d612ec6b","added_by":"auto","created_at":"2021-09-17 09:10:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1165913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-137286/v1/058b3faa-de17-4b4d-b568-81cd0f9a48b1.pdf"},{"id":4717261,"identity":"0ee72881-ed3f-4948-b9cc-6ef125445720","added_by":"auto","created_at":"2021-01-05 15:26:45","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":28388,"visible":true,"origin":"","legend":"","description":"","filename":"4Supplementalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-137286/v1/7224ff37746b1d9478d2a000.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAcute Effect of Particulate Matter Pollution on Hospital Admissions for Stroke among Patients with type 2 Diabetes in Beijing, China, from 2014 to 2018\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026diams; The health effect of particulate matter pollution on stroke among the patients with comorbid type 2 diabetes (T2D) in developing countries has remained largely unknown.\u003c/p\u003e\n\u003cp\u003e\u0026diams;\u0026nbsp;Exposure to particulate matter is associated an increased risk of stroke admissions among the patient with comorbid T2D.\u003c/p\u003e\n\u003cp\u003e\u0026diams;\u0026nbsp;Linear\u0026nbsp;exposure-response curves were observed between PM\u003csub\u003e5\u003c/sub\u003e and PM\u003csub\u003e10 \u003c/sub\u003ewith stroke admissions among T2D patients.\u003c/p\u003e\n\u003cp\u003e\u0026diams;\u0026nbsp;The study provided evidence of the risk of comorbid T2D and stroke due to particulate matter pollution.\u003c/p\u003e"},{"header":"1. Introduction","content":" \u003cp\u003eDiabetes mellitus is the ninth major cause of death, and just under half a billion people worldwide were living with diabetes mellitus in 2019, 90% of whom had type 2 diabetes (T2D) (Saeedi et al., 2019a). Most patients with T2D have at least one complication, and stroke is the leading cause of mortality among these patients. China bears the largest stroke burden in the world, which has increased over the past 3 decades (Wang et al., 2017b), accounting for the leading cause of death and disability-adjusted life-years (Zhou et al., 2019). Over one-third of stroke patients have comorbid diabetes, leading to a poor prognosis (Lau et al., 2019). The morbidity of stroke was associated with the progression to diabetes (Xu et al., 2018). To reduce the prevalence of comorbid stroke and diabetes, it is vital to examine modifiable risk factors to formulate appropriate measures for prevention and control.\u003c/p\u003e \u003cp\u003eAir pollutants are common modifiable risk factors for both stroke and T2D (Huang et al., 2019; Paul et al., 2020). Several studies have indicated that ambient air pollution is an important determinant of the risk of stroke (Fisher et al., 2019). Recently, the effect of air pollution on diabetes has attracted increasing attention (Liang et al., 2019; Yang et al., 2018b). Globally, 3.2\u0026nbsp;million incident cases of diabetes were due to ambient fine particulate matter, leading to 8.2\u0026nbsp;million disability adjusted life years (Bowe et al., 2018). However, the adverse effects of air pollution on the morbidity of stroke among people with comorbidities in the real world have remained largely unknown. Recently, \u003cem\u003eXue\u003c/em\u003e and colleagues conducted a case-crossover analysis of the China National Stroke Screening Survey and found that the first-ever stroke susceptibility associated with air pollution was significantly varied among Chinese adults (Xue et al., 2019). \u003cem\u003eLi\u003c/em\u003e, \u003cem\u003eet al.\u003c/em\u003e (Li et al., 2019) reported that people with cardiometabolic comorbidities, especially diabetes, were more vulnerable to air pollution. Epidemiological studies suggest that participants with T2D have lower vascular reactivity and thus may be more susceptible to cardiovascular effects related to air pollution (O'Donnell et al., 2011; O'Neill et al., 2005; Villeneuve et al., 2012).\u003c/p\u003e \u003cp\u003eHowever, evidence of the association between air pollution and of the risk of stroke among T2D patients is still lacking in China. The burden of comorbidities has become a serious public health problem in many large cities, especially Beijing. However, the acute effects of ambient particulate matter exposure on stroke subtype admissions among T2D participants remain unknown in China.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to examine the associations between ambient particulate matter (fine particulate matter with an aerodynamic diameter\u0026thinsp;\u0026lt;\u0026thinsp;2.5\u0026nbsp;\u0026micro;m [PM\u003csub\u003e2.5\u003c/sub\u003e] and inhalable particulate matter with an aerodynamic diameter\u0026thinsp;\u0026lt;\u0026thinsp;10\u0026nbsp;\u0026micro;m [PM\u003csub\u003e10\u003c/sub\u003e]) and total and subtype stroke admissions among patients with T2D in Beijing. In addition, differences in sex and age groups were tested by stratified analyses.\u003c/p\u003e "},{"header":"2. Materials And Methods","content":" \u003cp\u003eBeijing, located in northern China, had a population of approximately 21.54\u0026nbsp;million in 2019. The total area of Beijing is 16, 410 square kilometers. Beijing has a rather dry, continental monsoon climate, with four distinct seasons including windy, cold winters and rainy, hot summers.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Hospital admission data\u003c/h2\u003e \u003cp\u003eStoke admission records of patients with T2D were collected from the Beijing Municipal Health Commission Information Center (BMHCIC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phic.org.cn/\u003c/span\u003e\u003c/span\u003e) between Jan 1st, 2014, and Dec 31st, 2018. As a government agency, BMHCIC administers governmental hospitals, covering approximately 95% of medical services for permanent residents; it should be highly representative of Beijing permanent residents. The geographic locations of the 258 hospitals included in this study have been described elsewhere (Li et al., 2018). The data recording system has shown high validity in our previous study (Aklilu et al., 2020).\u003c/p\u003e \u003cp\u003ePatient data captured from the medical record system included date of admission, T2D status, sex, age, principal diagnosis and secondary diagnosis on discharge. A history of T2D was coded as E12 based on the secondary diagnosis according to the International Classification of Diseases 10th Revision (ICD-10) codes and was defined as having fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.1\u0026nbsp;mmol/L and/or current treatment of diabetes with antidiabetic medication before admission. The subtypes of the stroke hospitalizations were classified as hemorrhagic stroke (ICD-10: I60-I62, HS) and ischemic stroke (ICD-10: I63, IS). In the present study, total stroke admissions were calculated as the sum of HS and IS. Only stroke admissions among residents living in Beijing were included in the analysis (Figure S1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Air pollutants and meteorological conditions data\u003c/h2\u003e \u003cp\u003eDaily concentrations of air pollutants including PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), ozone (O\u003csub\u003e3\u003c/sub\u003e), and carbon monoxide (CO) in Beijing were retrieved from the Beijing Environmental Protection Bureau (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bjepb.gov.cn/\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003efrom Jan 1st, 2014, to Dec 31st, 2018. The average concentration of air pollutant data collected was based on 35 ambient air quality monitoring stations. These stations are distributed in 16 districts of Beijing city. The geographic locations of the 35 monitoring stations included in this study have been described elsewhere and have been proven to have good reliability and validity (Huang et al., 2015).\u003c/p\u003e \u003cp\u003eValues for the daily mean temperature and relative humidity data used in the model to adjust for confounders were obtained from the China Meteorological Data Sharing Service System online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.cma.cn/en\u003c/span\u003e\u003c/span\u003e) over the same time period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ethical clearance\u003c/h2\u003e \u003cp\u003eThe study was approved by the Institutional Review Board of Capital Medical University (No. IRB00009511). Informed consent was not specifically required since personal identifiers were not collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eAs the daily counts of hospital admissions generally followed a Poisson distribution (Yu et al., 2019), we applied an over-dispersed generalized additive model (GAM) to examine the short-term effect of particulate matter on stroke admissions among people with T2D (Song et al., 2018). Specifically, covariates were included in the main model as follows: (1) natural smooth functions of temperature and relative humidity with 3 degrees of freedom (\u003cem\u003edf\u003c/em\u003e) to control for the nonlinear confounding effects of weather factors (Wang et al., 2017a); (2) indicator variables for public holidays and day of week; and (3) a natural cubic spline function with 7 \u003cem\u003edf\u003c/em\u003e for calendar time to exclude unmeasured long-term and seasonal trends of daily stroke admissions (Chen et al., 2018b).\u003c/p\u003e \u003cp\u003eThe exposure-response curves were plotted between particulate matter variables and daily stroke admissions of patients with T2D. According to a previous study (Chen et al., 2018a), a natural spline function of 3 \u003cem\u003edf\u003c/em\u003e was added into the model. According to the Akaike information criterion, generalized cross validation, and partial autocorrelation function, multiple lag structures including single-day lags (from 0 to 7) and moving average exposure of multiple days (lag 0\u0026ndash;1, 0\u0026ndash;2, 0\u0026ndash;3, 0\u0026ndash;4, 0\u0026ndash;5, 0\u0026ndash;6, 0\u0026ndash;7) were used to determine the best lag structure for estimation.\u003c/p\u003e \u003cp\u003eSensitivity analyses were conducted to examine the robustness of the effect. First, we set the \u003cem\u003edf\u003c/em\u003e value from 4 to 10 per year. Second, principal component analysis was applied to cope with collinearity among air pollutants to estimate the independent effects of particulate matter (Feng et al., 2019) (Stafoggia et al., 2017). For instance, to estimate the independent effects of PM\u003csub\u003e2.5\u003c/sub\u003e, we first substituted all primary air pollutants (PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, and CO) with a composite latent variable (a linear combination of all air pollutants) through principal component analysis, and then included the latent variable in the conditional GAM model. Then the coefficient β\u0026prime; of the latent variable was transformed into the coefficient β of the original air pollutants.\u003c/p\u003e \u003cp\u003eThe data were also classified according to sex, age, subtype and T2D comorbid conditions. Patients were classified into two age groups: younger (18\u0026ndash;64\u0026nbsp;years) and elderly (\u0026ge;\u0026thinsp;65\u0026nbsp;years). Z tests were used to compare differences between effect estimates in the strata (Lin et al., 2016).\u003c/p\u003e \u003cp\u003eThe statistical tests were two-sided, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were conducted in R software (version 4.0.2) using the MGCV package. The effects are described as the percent changes and 95% CI in daily count on admissions for stroke per 10\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e among patients with T2D.\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":"\u003cp\u003eA total of 159,298 admissions for stroke were extracted from BMHCIC between 2014 and 2018; after excluding nonlocal admissions, 149,757 (94%) hospitalizations for ischemic stroke were included. Male and elderly (\u0026ge;\u0026thinsp;65\u0026nbsp;years) participants accounted for 58% and 61%, respectively. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the characteristics and distribution of daily stroke admissions, stratified by subtype, sex, and age.\u003c/p\u003e\n\u003cp\u003eThe distribution of air pollutants, meteorological factors, and the counts of daily admissions for stroke across four seasons from 2014 to 2018 are described in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The annual average values of daily mean concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e were 72.1\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 104.5\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively. The concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e were 1.5 and 2.1 times the Chinese Ambient Air Quality Standards limit, respectively. The median temperature was 14.4\u0026nbsp;\u0026deg;C, and the average relative humidity was 54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2%. Air pollutants except O\u003csub\u003e3\u003c/sub\u003e were strongly correlated with each other (coefficients distributed from 0.49 to 0.87) and moderately correlated with temperature and relative humidity (coefficients distributed from \u0026minus;\u0026thinsp;0.41 to 0.33) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistribution characteristics of daily stroke events among T2D patients (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;159, 298)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOverall stroke\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIschemic stroke\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHemorrhagic stroke\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159298 (100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u0026ndash;110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149757 (94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u0026ndash;104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9541 (6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u0026ndash;7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92977 (58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u0026ndash;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87285 (58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u0026ndash;61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5692 (60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66317 (42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u0026ndash;46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62469 (42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u0026ndash;44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3848 (40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u0026ndash;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61672 (39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u0026ndash;43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57454 (38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u0026ndash;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4218 (44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97626 (61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u0026ndash;68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92303 (62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u0026ndash;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5323 (56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eNote: No. (%) -counts of event and percentage, P\u003csub\u003e25\u003c/sub\u003e\u0026ndash;P\u003csub\u003e75\u003c/sub\u003e- 25th percentile and 75th percentile.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e show the effect estimates of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations on daily stroke admissions among T2D patients according to subtype, sex, and age. Overall, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e were significantly associated with hospitalizations for stroke among T2D patients. According to the smallest parameter value, lags of 4 days (lag4) were determined to be the best lag structure for both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e. A 10\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e2.5\u003c/sub\u003e (lag4) and PM\u003csub\u003e10\u003c/sub\u003e (lag4) corresponded to 0.14% (95% CI: 0.05%-0.23%) and 0.14% (95% CI: 0.06%-0.22%) incremental increased in stroke admissions among diabetic patients, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePercentage changes (95% CI) in stroke admissions per 10 ug/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e2.5\u003c/sub\u003e among T2D patients grouping by sex, age, and subtype.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLag days\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIschemic stroke\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHemorrhagic stroke\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMen\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWomen\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAge 18\u0026ndash;64 y\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65 y\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.18 (-0.02, 0.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.28 (-0.90, 1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.47 (-0.36, 1.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.18 (-0.08, 0.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.18 (-0.14, 0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.39 (0.06, 0.71)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.06 (-0.20, 0.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 1 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01 (-0.10, 0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.19 (-0.55, 0.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.32 (-0.74, 0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 (-0.10, 0.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.03 (-0.20, 0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.10 (-0.07, 0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.05 (-0.19, 0.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 2 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01 (-0.08, 0.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.09 (-0.40, 0.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.14 (-0.51, 0.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.03 (-0.15, 0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.06 (-0.08, 0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06 (-0.08, 0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.02 (-0.14, 0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 3 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 (-0.05, 0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.28 (-0.59, 0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.35 (-0.72, 0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01 (-0.11, 0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.08 (-0.06, 0.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.12 (-0.02, 0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.01 (-0.13, 0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 4 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.14 (0.05, 0.23)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.12 (-0.19, 0.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10 (-0.26, 0.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.15 (0.03, 0.27)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.11 (-0.03, 0.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.21 (0.06, 0.35)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.09 (-0.03, 0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 5 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.10 (0.01, 0.19)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.26 (-0.04, 0.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.24 (-0.11, 0.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09 (-0.03, 0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.12 (-0.02, 0.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.12 (-0.02, 0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.09 (-0.03, 0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 6 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.11 (0.02, 0.19)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.05 (-0.25, 0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16 (-0.19, 0.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09 (-0.02, 0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.13 (-0.01, 0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.14 (0.00, 0.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.08 (-0.03, 0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 7 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07 (-0.02, 0.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.19 (-0.11, 0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26 (-0.09, 0.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07 (-0.04, 0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.06 (-0.07, 0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11 (-0.03, 0.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.04 (-0.07, 0.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;1 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10 (-0.07, 0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.62 (-0.04, 1.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.72 (0.02, 1.42)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (-0.11, 0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.08 (-0.18, 0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09 (-0.18, 0.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.11 (-0.11, 0.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;2 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09 (-0.10, 0.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.28 (-0.48, 1.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27 (-0.51, 1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13 (-0.13, 0.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.04 (-0.26, 0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18 (-0.13, 0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.04 (-0.21, 0.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;3 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02 (-0.16, 0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.31 (-0.37, 0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.46 (-0.29, 1.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06 (-0.19, 0.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.02 (-0.31, 0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.10 (-0.19, 0.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.03 (-0.26, 0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;4 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06 (-0.13, 0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.26 (-0.43, 0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35 (-0.43, 1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07 (-0.19, 0.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.06 (-0.24, 0.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18 (-0.14, 0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.00 (-0.25, 0.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;5 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02 (-0.22, 0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.51 (-0.18, 1.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51 (-0.29, 1.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.07 (-0.33, 0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.05 (-0.25, 0.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.10 (-0.22, 0.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.09 (-0.34, 0.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;6 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03 (-0.18, 0.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.34 (-0.38, 1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28 (-0.56, 1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.05 (-0.32, 0.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.14 (-0.18, 0.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.21 (-0.13, 0.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.08 (-0.35, 0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;7 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.03 (-0.25, 0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.43 (-0.33, 1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28 (-0.60, 1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11 (-0.39, 0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.07 (-0.27, 0.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06 (-0.30, 0.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.09 (-0.37, 0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e Statistically positive significant results at the 5% level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are indicated in bold. T2D: type 2 diabetes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePercentage changes (95% CI) in stroke admissions per 10 ug/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e10\u003c/sub\u003e among T2D patients grouping by sex, age, and subtype.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLag days\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIschemic stroke\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHemorrhagic stroke\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMen\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWomen\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAge 18\u0026ndash;64 y\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65 y\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17 (-0.12, 0.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16 (-0.13, 0.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.28 (-0.90, 1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.25 (-0.12, 0.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06 (-0.39, 0.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23 (-0.24, 0.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14 (-0.23, 0.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 1 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03 (-0.05, 0.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05 (-0.04, 0.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.19 (-0.55, 0.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05 (-0.07, 0.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02 (-0.12, 0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13 (-0.01, 0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02 (-0.14, 0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 2 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02 (-0.06, 0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02 (-0.05, 0.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.09 (-0.40, 0.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01 (-0.11, 0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06 (-0.06, 0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07 (-0.05, 0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02 (-0.12, 0.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 3 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06 (-0.02, 0.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.08 (0.00, 0.17)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e-0.28 (-0.59, 0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 (-0.06, 0.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09 (-0.03, 0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.15 (0.03, 0.28)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00 (-0.10, 0.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 4 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.14 (0.06, 0.22)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.14 (0.06, 0.22)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.12 (-0.19, 0.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.14 (0.04, 0.24)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.14 (0.02, 0.26)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.20 (0.08, 0.33)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.10 (0.00, 0.20)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 5 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.12 (0.04, 0.19)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.11 (0.03, 0.18)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.26 (-0.04, 0.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09 (-0.01, 0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.15 (0.04, 0.27)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.13 (0.01, 0.25)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.11 (0.01, 0.20)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 6 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.11 (0.04, 0.18)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.11 (0.04, 0.19)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.05 (-0.25, 0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08 (-0.01, 0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.15 (0.03, 0.26)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10 (-0.02, 0.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e0.12 (0.02, 0.21)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 7 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.08 (0.00, 0.16)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07 (-0.01, 0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.19 (-0.11, 0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10 (0.00, 0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06 (-0.06, 0.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08 (-0.04, 0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08 (-0.01, 0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;1 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14 (-0.03, 0.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11 (-0.06, 0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.62 (-0.04, 1.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16 (-0.05, 0.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10 (-0.16, 0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08 (-0.18, 0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17 (-0.04, 0.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;2 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15 (-0.03, 0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15 (-0.05, 0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.28 (-0.48, 1.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.18 (-0.07, 0.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (-0.17, 0.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.24 (-0.06, 0.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10 (-0.14, 0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;3 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10 (-0.07, 0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08 (-0.09, 0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.31 (-0.37, 0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (-0.09, 0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06 (-0.20, 0.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.19 (-0.08, 0.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 (-0.18, 0.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;4 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11 (-0.06, 0.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10 (-0.08, 0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.26 (-0.43, 0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09 (-0.13, 0.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13 (-0.14, 0.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23 (-0.04, 0.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03 (-0.19, 0.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;5 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01 (-0.16, 0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02 (-0.20, 0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.51 (-0.18, 1.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.05 (-0.28, 0.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09 (-0.17, 0.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (-0.16, 0.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06 (-0.28, 0.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;6 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 (-0.14, 0.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02 (-0.16, 0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.34 (-0.38, 1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.05 (-0.29, 0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17 (-0.10, 0.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21 (-0.08, 0.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06 (-0.29, 0.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLag 0\u0026ndash;7 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.08 (-0.27, 0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11 (-0.30, 0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e0.43(-0.33, 1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.16 (-0.40, 0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 (-0.26, 0.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03 (-0.27, 0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.15 (-0.39, 0.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e Statistically positive significant results at the 5% level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are indicated in bold.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSpecifically, for PM\u003csub\u003e2.5\u003c/sub\u003e, a 10\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase corresponded to a 0.72% (95% CI: 0.02%-1.42%) incremental increase in hemorrhagic stroke (lag 0\u0026ndash;1). The incremental increase in hemorrhagic stroke was 0.15% (95% CI: 0.03%-0.27%) in men (lag 4), and 0.39% (95% CI: 0.06%-0.71%) in participants aged 18\u0026ndash;64\u0026nbsp;years (lag 0) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). For PM\u003csub\u003e10\u003c/sub\u003e, a 10\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in the four-day moving average corresponded to a 0.14% (95% CI: 0.06%-0.22%) incremental increase in ischemic stroke. The effect size was 0.14% (95% CI: 0.04%-0.24%) for men and 0.15 (95% CI: 0.04%-0.27%) for women, and the difference was not statistically significant (\u003cem\u003eZ\u003c/em\u003e=-0.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.77). The effect size in participants aged 18\u0026ndash;64\u0026nbsp;years was significantly higher than that in participants aged\u0026thinsp;\u0026ge;\u0026thinsp;65\u0026nbsp;years [0.20% (95% CI: 0.08%-0.33%) vs. 0.12% (95% CI: 0.02%-0.21%), \u003cem\u003eZ\u003c/em\u003e=-1.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10] (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Subgroup analysis indicated that the onset of the maximum effect of PM\u003csub\u003e10\u003c/sub\u003e was much earlier for males and younger adults than for females and elderly adults (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe exposure-response curves of the overall association between PM\u003csub\u003e2.5\u003c/sub\u003e and stroke admissions among T2D patients are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Monotonic increasing curves were observed overall and in each of the subgroups of T2D patients, which clearly indicated that the effect of PM\u003csub\u003e2.5\u003c/sub\u003e on stroke is linear. Evidently, high risks were observed for high PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, with flat slopes at low concentrations and steeper slopes for concentrations\u0026thinsp;\u0026ge;\u0026thinsp;200\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe exposure-response curves for the overall association between PM\u003csub\u003e10\u003c/sub\u003e and stroke admissions among T2D patients are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Monotonic increasing curves were observed overall and in subgroups of T2D patients, which indicated that the effect of PM\u003csub\u003e10\u003c/sub\u003e on stroke is almost linear. Evidently, high risks were observed for high PM\u003csub\u003e10\u003c/sub\u003e concentrations, with flat slopes at low concentrations and steeper slopes for concentrations\u0026thinsp;\u0026ge;\u0026thinsp;200\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":" \u003cp\u003eTo the best of our knowledge, this is the first study that attempted to determine associations between particulate matter pollution and stroke admissions among patients with T2D in China. Generally, this research showed that short-term exposure to particulate matter pollution was associated with an increased risk of stroke admissions among patients with T2D. The associations between particulate matter and stroke admissions in patients with T2D varied by sex, age and subtype, and the associations were robust when adjusted for copollutants. This study added to the limited evidence of acute effects of particulate matter pollution in populations with comorbidities in developing countries.\u003c/p\u003e \u003cp\u003eBetween 2014 and 2018, the average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e (72.1\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) and PM\u003csub\u003e10\u003c/sub\u003e (104.5\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) in Beijing far exceeded the Chinese National Ambient Air Quality Standards (35\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 70\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively). Meanwhile, T2D has become the leading risk factor for cerebrovascular disease in the Chinese population. It is estimated that 116.4\u0026nbsp;million Chinese adults had T2D in 2019, ranking first in the world (Saeedi et al., 2019b). Although the adverse effect attributable to PM exposure might be small in an individual, the overall attributable risk might be considerably higher, given the high pollutant concentrations and large population of T2D in China (Maji et al., 2017; Maji et al., 2018).\u003c/p\u003e \u003cp\u003eThe number of studies on the short-term effect of particulate matter on stroke has increased during the past few years. Significant associations were observed between particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e) concentrations and admissions for stroke, which are roughly consistent with results from previous studies (Huang et al., 2016; Tian et al., 2017). Liu \u003cem\u003eet al\u003c/em\u003e found that an interquartile range increase in PM\u003csub\u003e2.5\u003c/sub\u003e (47.5\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) and PM\u003csub\u003e10\u003c/sub\u003e (76.9\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) was significantly associated with admissions for ischemic stroke, with per centage changes of 1.0% (95% CI: 0.7%-1.4%) and 0.8% (95% CI: 0.3%-1.3%), respectively (Liu et al., 2017b). Previous research showed that short-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e was positively related to stroke admission and mortality (RRs: 1.011 (95% CI: 1.011\u0026ndash;1.012) and 1.003 (95% CI: 1.002\u0026ndash;1.004), respectively) (Shah et al., 2015). An interquartile range of PM\u003csub\u003e10\u003c/sub\u003e corresponded to 0.7% (95% CI: 0%-1.4%) increases in admissions for ischemic stroke (Liu et al., 2017a).\u003c/p\u003e \u003cp\u003eWe found that for patients with comorbid T2D, the effect estimates for both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e using moving average lags were much higher than those using single day lags. Most previous studies were designed to study the effects of particulate matter on the morbidity or mortality of stroke. Our study suggests that for participants with T2D, more attention should be paid to the health hazards of air pollution. Similarly, Yang \u003cem\u003eet al.\u003c/em\u003e (Yang et al., 2020) found that patients with T2D might be more vulnerable to air pollutant exposure, and Paul \u003cem\u003eet al.\u003c/em\u003e found that exposure to PM was associated with an increased risk of cardiovascular diseases among populations with prevalent T2D (Paul et al., 2020).\u003c/p\u003e \u003cp\u003eEven if the exact mechanisms behind the association of air pollution with increased admission in the T2D group have not been determined, there is growing evidence suggesting that the diabetes effect of PM pollution might be caused by endothelial dysfunction and systemic proinflammatory processes, leading to insulin resistance (Rajagopalan et al., 2018). Alterations in metabolic and inflammatory processes are crucial intermediate steps in explaining how PM exposure increases the risk of T2D, which is characterized by low-grade systemic inflammation (Lao et al., 2019). Previous research suggests that direct exposure of the respiratory tract to PM produces local inflammatory responses in lung tissue (Honda et al., 2017). It is hypothesized that upon chronic PM exposure, these local inflammatory processes spread to other organ systems and then lead to blood glucose metabolism abnormalities (Hotamisligil, 2017). A recent study indicated that adiponectin may be a potential mediator along the causal pathway (Lucht et al., 2020). These mechanisms might overlap at different times, and further studies are warranted to clarify them in detail.\u003c/p\u003e \u003cp\u003eThe effect size of PM\u003csub\u003e2.5\u003c/sub\u003e was much higher than that of PM\u003csub\u003e10\u003c/sub\u003e, which is generally consistent with existing studies (Huang et al., 2016; Zeng et al., 2018). Several mechanisms were proposed to explain the difference. First, PM\u003csub\u003e2.5\u003c/sub\u003e can penetrate the systemic circulation and then reach their way into the alveoli as well, whereas PM\u003csub\u003e10\u003c/sub\u003e can enter only the respiratory tract. Second, PM\u003csub\u003e2.5\u003c/sub\u003e has a higher particulate number and larger active surface area than PM\u003csub\u003e10\u003c/sub\u003e. Third, the larger surface area could carry a much larger volume of toxic pollutants to generate proinflammatory responses.\u003c/p\u003e \u003cp\u003eConsistent with a previous study (Zeng et al., 2018), higher effect estimates were reported in younger adults (18\u0026ndash;64\u0026nbsp;years). The reasons for the difference between age groups remain unknown. As air pollution has become an increasingly serious public health problem during the past few decades, and younger generations may suffer from air pollution earlier in their life cycle. Furthermore, older participants may pay more attention to preventing environmental hazards from PM pollution. For example, older participants are prone to stay at home during heavy pollution days.\u003c/p\u003e \u003cp\u003eThe present study showed the increased effect of air pollution exposure on stroke admissions among patients with T2D. The inhalation of toxic substances in individuals with compromised metabolic function due to diabetes may contribute to stroke by weakening the immune system and further decreasing metabolic function. In our study, the effect of particulate matter on admissions for overall stroke varied by sex among patients with T2D. Consistent with previous research, we found that PM\u003csub\u003e2.5\u003c/sub\u003e had stronger effects in male participants, whereas PM\u003csub\u003e10\u003c/sub\u003e had stronger effects in female participants. \u003cem\u003eHwang\u003c/em\u003e and colleagues demonstrated that the sex-specific effects could be explained by biological differences as well as differences in social exposures (Hwang et al., 2020). Generally, lung size, gas-blood barrier permeability and inflammation all vary by sex. Xia reported that, among surviving stroke patients, women were more susceptible to diabetes than men (Xia et al., 2019). Furthermore, men participate in more outdoor activities with fewer personal safeguard measures (e.g. wearing particulate-filtering masks), leading to increased exposure to particulate matter (Yang et al., 2018a).\u003c/p\u003e \u003cp\u003eThe determination of exposure-response relationships is critical for public health assessment of those with preexisting noncommunicable diseases. In our study, linear associations were observed between particulate matter and daily stroke admissions among patients with T2D. Consistent with a previous study, monotonically increasing curves were observed overall and in each of the subgroups of T2D patients which clearly suggests that the effect of both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e on stroke is linear (Costa et al., 2017). Particulate matter concentrations should be further limited to protect people with T2D, although exposure-response relationships may differ by air pollution mixture, meteorological conditions, and population susceptibility (Song et al., 2019).\u003c/p\u003e \u003cp\u003eThe study had some limitations. First, we could not collect data on the relationships between patients. However, there might be a few family aggregation cases, which may affect the population sensitivity (Yang et al., 2018c). Second, we used fixed-site monitor measurements as a proxy for personal exposure, which may result in exposure errors and underestimation of the associations between ambient air pollution and diseases. Third, the generalizability of our results might be limited as the study collected data from only one highly polluted city.\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eThis study suggests that short-term exposure to ambient PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e significantly increased the risk of stroke admissions among patients with T2D. Linear exposure-response curves were observed for PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in relation to stroke admissions among T2D patients, with flat slopes at low concentrations and steeper slopes for high concentrations. High particulate matter concentrations might be one risk factor for stroke among patients with T2D in Beijing, China. The study provided evidence of the risk of comorbidities due to particulate matter pollution.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eT2D: type 2 diabetes; PM\u003csub\u003e2.5\u003c/sub\u003e: fine particulate matter with an aerodynamic diameter \u0026lt; 2.5 \u0026mu;m; PM\u003csub\u003e10\u003c/sub\u003e: inhalable particulate matter with an aerodynamic diameter \u0026lt;10 \u0026mu;m; 95% CI: 95% confidence interval; BMHCIC: the Beijing Municipal Health Commission Information Center; ICD-10: the International Classification of Diseases, 10\u003csup\u003eth\u003c/sup\u003e Revision; HS: hemorrhagic stroke; IS: ischemic stroke; NO\u003csub\u003e2\u003c/sub\u003e: nitrogen dioxide; SO\u003csub\u003e2\u003c/sub\u003e : sulfur dioxide; O\u003csub\u003e3\u003c/sub\u003e: ozone; CO: carbon monoxide; GAM: generalized additive model; \u003cem\u003edf\u003c/em\u003e: degrees of freedom; SD: standard deviation; IQR: interquartile range.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe acknowledge the Beijing Municipal Health Commission Information Center and the Beijing Air Pollution and Metrological Data Center.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (No. 82003559), Nature Science Foundation of Capital Medical University (No. PYZ2018046), Beijing Municipal Training Project of Excellent Talents. The funding was neither used for the study design nor data collection but to cover for the publication fees.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data can be accessed from the Beijing Municipal Health Commission Information Center with permission via direct request.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eXiangtong Liu:\u0026nbsp;Conceptualization, Methodology, Data curation, Writing- Original draft preparation, Funding acquisition; Zhiwei Li: Methodology, Software,\u0026nbsp;Data curation, Visualization; Moning Guo: Data curation, Resources; Jie Zhang: Supervision, Data curation; Lixin Tao: Writing- Reviewing and Editing; Xiaolin Xu: Writing- Reviewing and Editing; Aklilu Deginet: Writing- Reviewing and Editing; Feng Lu: Resources; Yanxia Luo: Conceptualization, Methodology; Mengmeng Liu: Formal analysis; Mengyang Liu: Formal analysis; Yue Sun: Formal analysis; Haibin Li: Formal analysis; Xiuhua Guo: Supervision, Writing- Reviewing and Editing. All authors were involved with the critical revision of the manuscript and approved the final version.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eIn this study, informed consent was not specifically required because we did not use personal data identifiers. However, the study was approved by the Institutional Review Board of Capital Medical University with the IRB00009511 identification number.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAklilu, D., et al., 2020. Short-term effects of extreme temperatures on cause specific cardiovascular admissions in Beijing, China. Environ Res. 186\u003cstrong\u003e,\u003c/strong\u003e 109455.\u003c/li\u003e\n\u003cli\u003eBowe, B., et al., 2018. 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Lancet. 394\u003cstrong\u003e,\u003c/strong\u003e 1145-1158.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Particulate matter, Stroke, Type 2 diabetes, Comorbidities","lastPublishedDoi":"10.21203/rs.3.rs-137286/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-137286/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The health effect of particulate matter pollution on stroke has been widely examined; however, the effect among patients with comorbid type 2 diabetes (T2D) in developing countries has remained largely unknown.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A time-series study was conducted to investigate the short-term effect of fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) and inhalable particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) on hospital admissions for stroke among patients with T2D in Beijing, China, from 2014 to 2018. An over-dispersed Poisson generalized additive model was employed to adjust for important covariates, such as weather conditions and long-term and seasonal trends. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 159,298 (58% male) hospital admissions for stroke were reported. Linear\u0026nbsp;exposure-response curves were observed for PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10 \u003c/sub\u003ein relation to stroke admissions among T2D patients. A 10 μg/m\u003csup\u003e3\u003c/sup\u003e increase in the four-day moving average of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10 \u003c/sub\u003ewas associated with 0.14% (95% confidence interval [CI]: 0.05%-0.23%) and 0.14% (95% CI: 0.06%-0.22%) incremental increases in stroke admissions among T2D patients, respectively. A 10 μg/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e2.5 \u003c/sub\u003ein the two-day moving average corresponded to a 0.72% (95% CI: 0.02%-1.42%) incremental increase in hemorrhagic stroke, and a 10 μg/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e10 \u003c/sub\u003ein the four-day moving average corresponded to a 0.14% (95% CI: 0.06%-0.22%) incremental increase in ischemic stroke. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: High particulate matter might be a risk factor for stroke among patients with T2D.\u0026nbsp;PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e have a linear exposure-response relationship with stroke among T2D patients. The study provided evidence of the risk of comorbid T2D and stroke due to particulate matter pollution.\u003c/p\u003e","manuscriptTitle":"Acute Effect of Particulate Matter Pollution on Hospital Admissions for Stroke among Patients with type 2 Diabetes in Beijing, China, from 2014 to 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-01-05 15:26:33","doi":"10.21203/rs.3.rs-137286/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0726b646-8f9a-44ac-bd36-2717fbdbcb88","owner":[],"postedDate":"January 5th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1687118,"name":"Environmental Engineering"},{"id":1687119,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2021-01-05T15:26:45+00:00","versionOfRecord":[],"versionCreatedAt":"2021-01-05 15:26:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-137286","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-137286","identity":"rs-137286","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}