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Air Temperature and Inflammatory Responses in Myocardial Infarction Survivors

Schneider, Alexandraa; Panagiotakos, Demosthenesb; Picciotto, Sallyc; Katsouyanni, Kleab; Löwel, Hannelorea; Jacquemin, Bénédicted; Lanki, Timoe; Stafoggia, Massimoc; Bellander, Tomf,g; Koenig, Wolfgangh; Peters, Annetteafor the AIRGENE Study Group

doi: 10.1097/EDE.0b013e31816a4325
Original Article: ENVIRONMENT
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Background: Temperature changes have been associated with increased cardiovascular risk, but the role of inflammatory markers in this relationship is not well understood. The objective of this study was to analyze the association between air temperature and C-reactive protein, interleukin-6 and fibrinogen in postmyocardial infarction patients.

Methods: In a multicenter panel study, the 3 inflammatory blood markers were measured repeatedly. In total, 5813 blood samples in 1003 subjects were collected in 6 European cities representing different climates. Data on patient characteristics and disease history were gathered at the baseline visit. Meteorologic data were obtained from the city-specific network stations. The association was analyzed using a semiparametric model with random patient effects.

Results: A 10°C decrease in the 5-day-average of air temperature before the blood withdrawal was associated with a 4% increase in C-reactive protein (4.3% [95% confidence interval = 0.2% to 8.1%]). Correspondingly, an increase of interleukin-6 was observed for the same time window (3.3% [0.1% to 6.3%]) whereas fibrinogen showed an increase of 1.3% (0.2% to 2.4%) with a lag of 3 days.

Conclusion: A decrease in air temperature, particularly the average temperature of the last 5 days, was associated with an increase in both C-reactive protein and interleukin-6, whereas fibrinogen seemed to react to temperature changes after 3 days. In susceptible patients this might lead to an additional risk for cardiovascular events and suggests a biologic mechanism for the observed seasonal variation in death from ischemic heart disease and stroke in the elderly.

Supplemental Digital Content is Available in the Text.

From the aHelmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Epidemiology, Neuherberg, Germany; bDepartment of Hygiene and Epidemiology, University of Athens, Athens, Greece; cLocal Health Authority Rome, Rome, Italy; dIMIM-Municipal Institute of Medical Investigation, Barcelona, Spain; eEnvironmental Epidemiology Unit, National Public Health Institute (KTL), Kuopio, Finland; fInstitute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden; gDepartment of Occupational and Environmental Health, Stockholm County Council, Stockholm, Sweden; and hDepartment of Cardiology, University of Ulm, Ulm, Germany.

Submitted 13 July 2007; accepted 10 December 2007; posted 1 April 2008.

The AIRGENE study was funded as part of the European Union's 5th Framework Program, key action number 4: “Environment and Health,” contract number QLRT-2002-02236.

Supplemental material for this article is available with the online version of the journal at www.epidem.com; click on “Article Plus.”

Correspondence: Alexandra Schneider, Institute of Epidemiology, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany. E-mail: alexandra.schneider@helmholtz-muenchen.de.

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In recent years, studies worldwide have indicated that variation in climatologic conditions is correlated with higher mortality and morbidity rates.1–3 In particular, temperature and, to a lesser extent, relative humidity have been found to interfere with health and disease, especially with cardiovascular disease (CVD).4,5 These results suggest that patients with ongoing CVD might benefit from possible precautions, especially during winter weather, as a means of reducing risk related to cold temperature exposures among the elderly.

However, the exact mechanisms that promote this association have not been fully understood. In a study of weather-related changes on the 24-hour blood pressure profile,6 the investigators reported that mean 24-hour systolic blood pressure was lower on hot days and higher on cold days when compared with intermediate days, but no relationship was found between air temperature and heart rate. Furthermore, Jehn et al7 studying men and women in the Dietary Approaches to Stop Hypertension (DASH) project, reported that the increase in cardiovascular mortality observed in winter could be attributed to an increase in blood pressure variability. In addition, low-grade systemic inflammation seems to play an essential role in the initiation, progression, and the final pathophysiological steps of atherosclerosis, plaque erosion, and eventually plaque rupture.8 It also participates in the development of obesity, insulin resistance, and hypertension.9–11 It has been suggested that in cold conditions fibrinogen as well as platelet count both are increased and promote clotting, while reduced plasma volume and increased blood viscosity during cold exposure also tend to promote thrombosis.12

However, the association between climatologic conditions, such as air temperature and humidity, and low-grade systemic inflammation has to our knowledge never been investigated, especially at population level, although seasonal variation has been demonstrated for fibrinogen. In this work we sought to evaluate whether air temperature was associated with inflammation indices, such as the acute phase proteins C-reactive protein and fibrinogen as well as the messenger interleukin-6 in myocardial infarction survivors from various parts of Europe within a repeated measurements framework. In addition we considered other weather parameters in sensitivity analyses to evaluate the role of temperature in the complex weather settings.

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METHODS

Study Population

A prospective longitudinal study (AIRGENE) of postmyocardial infarction (MI) patients was performed in 6 European cities—Athens (Greece), Augsburg (Germany), Barcelona (Spain), Helsinki (Finland), Rome (Italy), and Stockholm (Sweden)—chosen so as to include a variety of geographic and climatic conditions.13 Briefly, patients aged 35 to 80 years, who had MIs not longer than 6 years but at least 3 months ago, were recruited. The following patients were excluded: those who had undergone surgical procedures (percutaneous transluminal coronary angioplasty, bypass surgery) less than 3 months before the beginning of the study, those with chronic recurring inflammatory diseases (eg, Crohn's disease) and those who were taking anti-inflammatory medication that modifies the biomarkers were not included. Preferably, never-smokers and ex-smokers who had quit at least 3 months before the start of the study were recruited. The Southern centers also accepted light current smokers. Study protocols were approved at local human subjects committees, and written informed consent was obtained from all patients. All methods used in the study centers were conducted according to common standard operating procedures developed by the participating centers. A more detailed description of the patient recruitment and the studied panel can be found elsewhere.13

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Clinical Measurements

The patients were scheduled for 6 to 8 clinical visits between May 2003 and July 2004. The visits were conducted every 4 to 6 weeks on the same day and at the same time of the week to minimize the impact of circadian variation and day of the week. If patients suffered from acute infections such as a cold or influenza in the 3 days before the clinical visit, examinations were postponed.

At the first visit, a baseline questionnaire was administered regarding health status, pulmonary and cardiac symptoms, medication intake, and smoking history. Moreover, a blood pressure measurement was performed. Body mass index (BMI) was measured and a blood serum sample was drawn to assess glycosylized hemoglobin, a severity index for hyperglycemia and therefore a marker for the control of diabetes, N-Terminal proB-type natriuretic peptide, a marker for congestive heart disease,14 and baseline serum lipids.

A 7-day recall on medication intake was obtained and venous ethylenediamine tetraacetic-plasma samples were collected for the determination of the inflammatory markers C-reactive protein, interleukin-6, and fibrinogen in the central laboratory in Ulm, Germany. Details on storage, handling and processing of the blood samples can be found in the paper by Peters et al.13 Because of quality-control issues, fibrinogen could not be analyzed in Athens. Data on influenza virus occurrence during the winter months of the study period were obtained via medical surveillance networks in each city except for Athens.

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Meteorologic Data

The meteorologic parameters air temperature (temp), relative humidity, dew point temperature (dp) and barometric pressure were obtained through the city-specific air monitoring networks and the meteorological services. Apparent temperature (at) was calculated by using the formula at = −2.653 + (0.994 × temp) + (0.0153 × dp2).15,16 For valid 24-hour mean values, at least 75% of the hourly observations had to be present. Missing data on the aggregate level were replaced using an adapted formula based on the APHEA-Study.13,17

Moving averages of meteorologic parameters concurrent with the clinical examinations were used to characterize the population-average exposures, taking into account the time of the blood withdrawal. For each person and visit the individual 24-hour average exposure immediately preceding the clinical visit (lag 0) up to 4 days (lag 1–4) prior to the examination were calculated if more than 2/3 of the hourly measurements were available. In addition, the mean of lags 0 to 4 was calculated.

Furthermore, air pollution data (eg, hourly means of particles with aerodynamic diameter below 10 μm [PM10] and ultrafine particles) from fixed monitoring sites representing urban background concentrations were collected. Moving 24-hour averages were calculated in analogy to the meteorological parameters.

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Statistical Analyses

Data were analyzed using random effects models to account for repeated measures and for unobserved heterogeneity of the data. For the dependencies between the repeated measures in each patient, compound symmetry structure was assumed for the covariance matrix, as the half-lives of the markers (C-reactive protein: 19 hours, interleukin-6: 6 hours, fibrinogen: 4 to 6 days) were much shorter than the intervals between the visits. Penalized splines in the additive mixed models framework were used to allow for nonparametric exposure-response functions.18 C-reactive protein and interleukin-6 needed to be log-transformed to fulfil the model assumptions of residual normality in simple additive models.

City-specific confounder models without meteorologic parameters were built for each blood marker separately, identifying the time-invariant patient characteristics associated with the mean level of C-reactive protein, fibrinogen and interleukin-6 and leading to a normally distributed random patient intercept. In a first step, time-invariant factors were selected for all cities combined. Variables considered to impact the average blood marker concentrations included BMI, diabetes, pack-years of smoking, medication, physical activity, alcohol consumption, sex, and N-Terminal proB-type natriuretic peptide. As further possible indicators for blood marker levels, we also considered age, number of MIs, bronchitis (ever diagnosed), level of education, cholesterol (total and high density lipoprotein), glycosylized hemoglobin, blood pressure, symptoms of chronic obstructive pulmonary disease, self-reported health status, hypertension (ever diagnosed), arrhythmia (ever diagnosed), heart failure (ever diagnosed), and the presence of a cardiac pacemaker. In a second step for each city, a more parsimonious model was selected out of the formerly chosen all-cities variables and then time-varying risk factors were added. To assure an adjustment for season, long-term time trend was forced into all models. Moreover relative humidity was added to each model using the lag corresponding to the analyzed temperature lag. Additionally, time of the day and day of the week were included if this adjustment proved necessary. P-splines were used to model continuous confounding variables and were compared with linear terms and polynomials of degree 2 and 3. All decisions on goodness-of-fit were based on Akaike's Information Criterion. Details on the models can be found in the supplemental material, available with the online version of this article (eTable 1).

After completion of the confounder models, the effects of different air temperature and apparent temperature lags were estimated linearly as well as smoothly. In a further step, heterogeneity between centers was assessed according to the DerSimonian and Laird approach.19 City-specific effect estimates were pooled subsequently with fixed or random effects (decision based on the homogeneity test result with 90%-confidence interval [CI] to be more conservative) by using meta-analysis methodology.20,21 Data were analyzed using the statistical package SAS Version 9.1 (SAS Institute Inc., Cary, NC).

Sensitivity analyses were performed to explore the robustness of the models and the results. We used a more parsimonious model without adjustment for relative humidity. An additional adjustment for barometric pressure (corresponding lag to temperature) was tested. To account for seasonal variability, a dichotomous variable for cold season (October to March) and warm season (April to September) was introduced into the model, in addition to the trend variable. Categorical medication variables were selected using the same methods as for the other confounding variables, and these were added to the model of each city. Finally, a continuous variable for influenza occurrence during the study period was added as a smooth function to the core model of each city (except for Athens). A possible differing influence of hot and cold days (below the 10th percentile or above the 90th percentile of the city-specific air temperature distribution) was also checked by using indicator variables for such days and by analyzing smooth exposure-response-functions of air temperature. The influence of simultaneous immediate (lag 0) and cumulative temperature effects (mean of lag 0 to lag 3) in the model were analyzed. An additional sensitivity analysis was performed adjusting for air pollution effects. Effect modification was analyzed by sex, diabetic status (yes vs. no), BMI (underweight or normal vs. overweight or obese), and season (cold vs. warm).

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RESULTS

Study Population

The study population comprised 1003 MI survivors who fulfilled the inclusion and exclusion criteria and who had results for at least 2 valid, repeated blood samples. Table 1 presents the baseline characteristics of the study participants. Nearly all patients were treated for hypertension and a majority of the patients were treated with lipid reducers, as well as antithrombotic therapy to prevent future myocardial infarctions. A more detailed description of the patient characteristics can be found elsewhere.13 Two-thirds of the observations (n = 3802) were made in the cold season (October to March) and one-third (n = 2011) in the warm season (April to September).

TABLE 1

TABLE 1

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Meteorologic Parameters

The distributions of the 24-hour average concentrations of the meteorologic data are given in Table 2. Mean air temperature and apparent temperature increased from north to south, with Athens being the hottest city. Relative humidity was highest in Stockholm and Rome, whereas air pressure was highest in Augsburg. Relative humidity and barometric pressure showed very low correlation with the other meteorologic parameters. However, due to the above formula, apparent temperature was highly correlated with dew point temperature and air temperature (eTable 2).

TABLE 2

TABLE 2

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Inflammatory Markers

Based on the questionnaire data at the clinical visits, 255 of 6068 collected blood samples had to be excluded due to acute infections (cold/flu, urinary tract infection, gastro-intestinal infection, or acute respiratory infection) or surgical procedures (including severe dental intervention) 3 days before the clinical visit, which could have severely altered the concentrations of the inflammatory markers. Overall, 5813 plasma samples for inflammatory marker determination were available for analyses.

The description of the 3 inflammatory markers across all cities can be found in Table 3. The geometric means over all observations of C-reactive protein, interleukin-6, and fibrinogen were 1.39 mg/L, 2.33 pg/mL, and 3.51 g/L, respectively. The 3 markers showed a moderate correlation, with the Spearman correlation coefficient ranging between 0.42 and 0.52 for all single measurements using the data of all cities combined. The correlation between the mean values per patient are presented in Table 3. A more detailed description of the city-specific blood markers and their correlation can be found elsewhere.13,22

TABLE 3

TABLE 3

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Regression Analyses

Pooled results for the regression of the 3 blood markers using the model with adjustment for relative humidity (main model) are summarized in Table 4. Due to the high correlation of air temperature and apparent temperature (data not shown) their effect estimates were quite similar, with the estimates for apparent temperature being a little smaller. Therefore, only the influence of air temperature will be discussed in more detail.

TABLE 4

TABLE 4

The increases of C-reactive protein-levels with a decrease in air temperature were similar for all analyzed lags. A decrease of 10°C on average led to an increase of 3% to 4% in the geometric mean of C-reactive protein. The strongest effects were found for lag 1, lag 4, and the 5-day-average. As with the C-reactive protein results, interleukin-6 was negatively associated with air temperature in the main model, with around 2% change in geometric mean. The strongest effect was seen for the 5-day-average. In contrast, fibrinogen seemed to react mainly with a lag of 3 days but also showed an increase of around 1% of the arithmetic mean with a decrease in air temperature for all other lags. The city-specific effect estimates and the pooled effect estimate for lag 1 and the 5-day-average for all 3 blood markers are presented in Figure 1.

FIGURE 1

FIGURE 1

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Sensitivity Analyses

The effect estimates of the analysis without adjustment for relative humidity (basic model) showed the same pattern, but were a little smaller and the confidence intervals included the null (Table 5). The additional adjustment for barometric pressure in the main model did not lead to any changes in the effect estimates. When adjusted for the influence of season, the increase in the inflammatory marker levels became even stronger, with a percent change of around 5% to 6% in the geometric mean of C-reactive protein. A more detailed analysis of the seasonal pattern of the inflammatory markers can be found in the online supplemental material (eTable 3 and eFigure 1). Introduction of smooth influenza data did not change the value of the effect estimates for C-reactive protein but led to loss in accuracy of the estimation probably due to the heterogeneity of the city-specific effect estimates. But when influenza was included as a confounder in the interleukin-6 model, all analyzed temperature lags showed strong effects on the inflammatory marker with higher effect estimates when compared with the main model. Using the Akaike's Information Criterion-selected medication categories as additional confounder variables, the pooled effect estimates remained robust, with more heterogeneity in the city-specific estimates and therefore a smaller increase in blood marker level. Only fibrinogen showed a more than doubled effect estimate in association with the mean temperature of the last 5 days when adjusted for medication.

TABLE 5

TABLE 5

The adjustment for the immediate effect of ultrafine particles on interleukin-6 led to negative effect estimates that remained negative, to with a loss in accuracy mainly due to around 750 missing observations in ultrafine particle measurements during the study period. Adjustment for the 5-day-average of PM10 in the fibrinogen model resulted in robust effect estimates, as only around 150 observations were missing (data not shown). However, the different changes in the confounder model showed that the observed increases in levels of C-reactive protein, interleukin-6, and fibrinogen remained robust and were not particularly dependent on the chosen model.

The analyses with special indicators for hot and cold days, as well as the simultaneous modeling of immediate and cumulative temperature influences, did not reveal any new information (data not shown). When looking at the smooth exposure-response functions, temperature effects were mainly linear, with a very slightly U-shaped curve for Rome C-reactive protein and fibrinogen data, as well as for Barcelona fibrinogen data. When analyzing interaction of temperature with sex, diabetic status, BMI, and season, no effect modification was apparent. The effect estimates were a little larger in size for men, for nondiabetics, for underweight or normal patients, and for the cold season—but also more heterogeneous between cities, resulting in broad confidence intervals (online supplemental material [eFigure 2]).

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DISCUSSION

The analyses revealed that a decrease in air temperature, particularly the average temperature of the last 5 days, leads to increased levels of C-reactive protein and interleukin-6, whereas fibrinogen seems to react to temperature changes after 3 days. In susceptible patients this might lead to an additional risk for cardiovascular events and suggests a biologic mechanism for the observed seasonal variation in death from ischemic heart disease and stroke. Our results indicate the stimulation of the interleukin-6 production with a decrease in temperature over the last 5 days, which is the first step in the stimulation of acute phase proteins in the liver. The acute phase proteins C-reactive protein and fibrinogen reacted correspondingly.

To our knowledge no studies have looked at low-grade systemic inflammation and air temperature at the population level, although many studies have observed the influence of weather changes on hospitalization and mortality. Our findings are in agreement with some previous research without controlling for air pollution. In particular, Danet et al23 have reported that a 10°C decrease in air temperature is associated with a 13% increase in daily rates of MI and coronary deaths. Similarly, a 1°C decrease in mean air temperature yielded a 5% increase in Acute Coronary Syndrome, in Athens, Greece.24 These results are not completely in accordance with the U-shaped relation between temperature and mortality from coronary artery disease that are reported from a study in Taiwan.25 Below the range of 26°C to 29°C that corresponded to a minimum of deaths, the risk increased by 2.8% per 1°C reduction. As an explanation for the absence of the ascending limb of the U-shaped curve in a more temperate climate, Fries et al26 have proposed that thermal stress caused by the atmospheric conditions may be too weak to influence the incidence of MI. Braga et al27 found that in colder cities both high and low temperatures were associated with increased CVD deaths, with the effect of cold temperatures persisting for days, whereas the effect of high temperatures was restricted to the day of the death or the day before, and included some harvesting. In comparison the hot-day effect was one-fifth the cold-day effect. In hot cities neither hot nor cold temperatures had much effect on CVD deaths. However, there have also been many studies on extreme high temperatures or heat waves and increased mortality.28–30 In this context it is important to note that in the AIRGENE study the entire summer was included only in Augsburg, Germany.

Several mechanisms are thought to be involved in the impact of air temperature on CVD. Environmental temperature has an inverse relation with blood pressure.6,31,32 An increased blood pressure decreases the ratio of myocardial oxygen supply to demand and may lead to myocardial ischemia, particularly in a vulnerable myocardium. Moreover, pulse pressure increases and this may result in arterial dissection and Acute Coronary Syndrome. Apart from the above, fibrinogen is inversely related to air temperature, but part of the rise may be the result of seasonal respiratory infections.32–36 However, in the presented study clinical visits have been postponed, observations have been excluded when patients showed any signs of infections, and adjustment for influenza was included to ensure no such influence on the inflammatory markers. In cold conditions the plasma concentrations of certain clotting factors, platelet count and their in vitro aggregation are all increased and promote clotting.33,37–40 Furthermore, reduced plasma volume and increased blood viscosity during cold exposure also tend to promote thrombosis.37,40 The role of these factors in atherogenesis is uncertain but cold can also adversely alter plasma lipid concentrations leading to a potential chronic atherogenic effect.32,33,35 In addition, C-reactive protein-levels seem to be seasonally dependent, with higher values during winter and spring41 and with cardiovascular events more prominent during the cold season. The acute changes in processes involved in atherothrombosis may acutely increase the risk of an event. Elevated single measurements of these risk factors, such as C-reactive protein, have been implicated in increasing the risk of cardiovascular events in cohort studies.42,43 Therefore, these recurrent acute changes during winter months may contribute to the progression of atherosclerosis. On the other hand, the relationship between temperature and Acute Coronary Syndrome may be indirectly causal. During winter months there may be increased indoor smoking, increased body weight due to a higher fat intake, or lower levels of physical activity and a higher rate of loneliness.

The Eurowinter Survey by Keatinge et al44 made clear that especially the elderly are an extremely vulnerable population group with regard to low ambient and room temperatures. Given the fact that the association between temperature and Acute Coronary Syndrome may be attributed to pathophysiological factors, it would be wise to take sufficient precautions against cold, such as adequate indoor heating, sufficient protective clothing, and the adoption of a healthier lifestyle during winter.

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Strengths and Limitations

The large cohort of vulnerable post MI patients with up to 8 consecutive measurements of inflammatory markers levels is the main strength of this study. As increased concentrations in C-reactive protein, interleukin-6, and fibrinogen have been associated with the risk of CVD and mortality,45–48 it is particularly necessary to study high-risk groups. However, this is also the reason why the studied patients were heavily medicated, in particular statins were taken by almost all subjects regularly. In addition, the multiethnic origin of the participants, the large range of European climates and the severe adjustments for confounding are strengths of this paper. We selected as our main model the more conservative model without adjustment for season because we wanted to avoid an overestimation of the temperature effect due to induced confounding as long-term time trend, temperature and season all compete for the same effect in the model.

Air pollution is a factor that has been associated with CVD mortality and morbidity, and it correlates well with air temperature. It is still unclear whether air pollutants are confounders or effect modifiers of the temperature-biomarker association. However, Ruckerl et al22 have shown an effect of ultrafine particles on interleukin-6 in the first 24 hours after air pollution exposure and an association of the 5-day-average of PM10 with fibrinogen in this same study population. Therefore, we additionally adjusted for particulate matter to disentangle the effects of temperature and air pollution on the inflammatory markers.

Nevertheless one limitation is that only outdoor temperature was measured while study subjects probably spent a lot of time indoors. However, as the number of cardiovascular events is increased in winter, it is very likely that the effect of cold temperature on inflammatory markers is linked to short exposures to outdoor temperatures. In the cities with warmer climates, the housing conditions and lower personal adjustment to cold temperatures might contribute to the increase of cardiovascular events in winter. This might also explain the strong effects on inflammatory markers in Rome as observed in this study (Fig. 1). Another limitation of the study is the relatively small number of observations in the really hot months of the year. This might be one reason why we have not found a U-shaped response curve, as has sometimes seen for the association between air temperature and mortality.25,49,50 Another reason for the mainly linear associations in this study might be that the study population consisted of vulnerable people, but not extremely sick people—because the latter would not have been able to come to the study centers for their visits, whereas mortality studies depend by their nature on very sick people.

The present work suggests that a decrease in air temperature is associated with an increase in inflammatory markers, which might lead to the promotion of atherosclerosis. Our findings offer evidence for temperature exposure playing a role in seasonal patterns of cardiovascular morbidity and mortality by examining markers of intermediate processes. Therefore, we added pathophysiologic plausibility for the effect of environmental conditions on the cardiovascular system that could be investigated in future prospective cohort studies.

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ACKNOWLEDGMENTS

The AIRGENE study group comprises the following partners: Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Epidemiology (Neuherberg, Germany): A. Peters (PI), M.-A. Bind, I. Brueske-Hohlfeld, H. Chavez, J. Cyrys, U. Geruschkat, H. Grallert, S. Greven, A. Ibald-Mulli, T. Illig, H. Kirchmair, S. von Klot, M. Kolz, M. Marowsky-Koeppl, M. Mueller, R. Rueckerl, A. Schaffrath Rosario, A. Schneider, H.-E. Wichmann; Institute of Health Economics and Health Care Management (Neuherberg, Germany): R. Holle, H. Nagl; KORA Study Center (Augsburg, Germany): I. Fabricius, C. Greschik, F. Günther, M. Haensel, U. Hahn, U. Kuch, C. Meisinger, M. Pietsch, E. Rempfer, G. Schaich, I. Schwarzwälder, B. Zeitler; KORA Myocardial Infarction Registry (Augsburg, Germany): H. Loewel; University of Ulm, Department of Cardiology (Germany): W. Koenig (PI), N. Khuseyinova, G. Trischler; Local Health Authority RME, Department of Epidemiology ASL RME (Rome, Italy): F. Forastiere (PI), P. Compagnucci, F. Di Carlo, M. Ferri, A. Montanari, C. Perucci, S. Picciotto, E. Romeo, M. Stafoggia; Catholic University (Rome, Italy): R. Pistelli, L. Altamura, M.R. Andreani, F. Baldari, F. Infusino, P. Santarelli; Hospital San Filippo Neri: A.P. Jesi; Italian National Institutes of Health (Rome, Italy): G. Cattani, A. Marconi; National Public Health Institute (KTL) (Kuopio/Helsinki, Finland): J. Pekkanen (PI), M. Alanne, H. Alastalo, T. Eerola, J. Eriksson, T. Kauppila, T. Lanki, P. Nyholm, M. Perola, V. Salomaa, P. Tiittanen; Helsinki University Central Hospital: K. Luotola; Institute of Environmental Medicine, Karolinska Institute (Stockholm, Sweden); Department of Occupational and Environmental Health, Stockholm County Council (Stockholm, Sweden); Department of Cardiology, Stockholm South Hospital (Stockholm, Sweden): T. Bellander (PI), N. Berglind, K. Bohm, R. Härdén, E. Lampa, P. Ljungman, F. Nyberg, B. Ohlander, G. Pershagen, M. Rosenqvist, T. Larsdotter Svensson, E. Thunberg, G. Wedeen; Municipal Institute of Medical Research (Barcelona, Spain): J. Sunyer (PI), M. Covas, M. Fitó, M. Grau, B. Jacquemin, J. Marrugat, L. Muñoz, G. Perelló, E. Plana, C. Rebato, H. Schroeder, C. Soler; University of Athens Medical School, Department of Hygiene and Epidemiology (Athens, Greece): K. Katsouyanni (PI), A. Chalamandaris, K. Dimakopoulou, D. Panagiotakos; Department of Cardiology (Athens, Greece): C. Stefanadis, C. Pitsavos, C. Antoniades, C. Chrysohoou, J. Mitropoulos; University of Helsinki, Department of Physical Sciences (Helsinki, Finland): M. Kulmala (PI), P. Aalto, P. Paatero.

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