Characteristics of participants with type 2 diabetes per geographic region (Nielsen area).

<p>Results are numbers (N), frequencies in % or means (SD). Abbreviation of Nielsen areas: 1 (N) = Hamburg, Bremen, Schleswig-Holstein, Lower Saxony (North); 2 (W) = North Rhine-Westfalia (West); 3 (SW) = Hesse, Rhineland-Palatinate, Saarland, Baden-Württemberg (Southwest); 4 (S) = Bavaria (South); 5 (B) = Berlin (Northeast); 6 (NE) = Mecklenburg-Vorpommern, Brandenburg, Saxony-Anhalt (Northeast); 7 (E) = Thuringia, Saxony (East). HbA1c levels in % (NGSP) can be converted to mmol/mol (IFCC) by application of the following formula: IFCC = (10.93*NGSP)−23.50.</p

Adjusted mean HbA_1c and difference in HbA_1c comparing quartiles of particulate matter (PM₁₀) exposure in type 2 diabetes patients^*.

<p>*Results are adjusted means for HbA_1c in % calculated from generalized linear regression models. Models were fitted adjusting for age, sex, body mass index, duration of diabetes, geographic region, year of treatment, and social indicators (low education, immigration background). Furthermore, difference in HbA_1c levels in % (95% CI) comparing quartiles of PM₁₀ exposure also derived from linear regression models are presented. Group differences are considered as significant (highlighted in bold) if corresponding 95% confidence intervals do not include 0. HbA1c levels in % (NGSP) can be converted to mmol/mol (IFCC) by application of the following formula: IFCC = (10.93*NGSP)−23.50.</p

Characteristics of participants with type 2 diabetes^*.

<p>*Results are numbers (N), frequencies in % or means (SD). HbA1c levels in % (NGSP) can be converted to mmol/mol (IFCC) by application of the following formula: IFCC = (10.93*NGSP)−23.50.</p

Association between circulating immune mediators and IGM.

<p>Odds ratios are adjusted for age, BMI, smoking status, passive smoking, education, exposure to indoor mould and season of blood sampling.</p

Impact of adjustment for immune mediators on the relationship between IGM and NO₂^†.

<div><p>*Adjusted for age, BMI, smoking status, passive smoking, education, exposure to indoor mould and season of blood sampling. </p> <p>All additional models are adjusted for the aforementioned covariables and the immune mediator indicated on the x-axis.</p></div

Association between circulating immune mediators and NO₂^†.

<p>Mean ratios are adjusted for age, BMI, smoking status, passive smoking, education, exposure to indoor mould and season of blood sampling. Ratios represent the relative increase of the particular serological marker concentration by 1-IQR increase of NO₂^† levels (IQR=14.65 µg/m³).</p

Characteristics of the Study Population Stratified by Fasting Glucose Levels.

<p>The table shows characteristics of the study population: n = 30 with normal fasting glucose, n = 30 with impaired fasting glucose. Data represent mean values ± standard deviation, median with 25^th and 75^th percentiles, or percentages. The p value indicates statistical differences between both groups for the respective variables.</p><p>Characteristics of the Study Population Stratified by Fasting Glucose Levels.</p

Correlation Between Plasma Levels of AGEs.

<p>Correlation coefficients r are presented with their corresponding p value. n, number of observations;</p><p>** p value <0.05;</p><p>* p-value between 0.05 and 0.1.</p><p>Correlation Between Plasma Levels of AGEs.</p

Plasma Concentrations of compounds of protein glycation and oxidation.

<p>Data are given as median with 25th and 75th percentiles;</p><p>* unadjusted p value;</p><p>** p value adjusted for age, BMI, smoking status, passive smoking and education.</p><p>3-DF, 3-deoxyfructose; 3-DPs, 3-deoxypentosone; 3-DP, 3-deoxypentulose; MGH1, methylglyoxal-derived hydroimidazolone 1; 3-DGH, 3-deoxyglucosone-derived hydroimidazolone; CML, carboxymethyl lysine; CEL, carboxyethyl lysine; MetSO, methionine sulfoxide; FruLys, Nε-fructosyllysine.</p><p>Plasma Concentrations of compounds of protein glycation and oxidation.</p

Development of Land Use Regression Models for PM_2.5, PM_2.5 Absorbance, PM₁₀ and PM_coarse in 20 European Study Areas; Results of the ESCAPE Project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM_2.5, PM_2.5 absorbance, PM₁₀, and PM_coarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (<i>R</i>²) was 71% for PM_2.5 (range across study areas 35–94%). Model <i>R</i>² was higher for PM_2.5 absorbance (median 89%, range 56–97%) and lower for PM_coarse (median 68%, range 32– 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower <i>R</i>² was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation <i>R</i>² results were on average 8–11% lower than model <i>R</i>². Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE