Heat and risk of myocardial infarction: hourly level case-crossover analysis of MINAP database.
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ABSTRACT: Objective
To quantify the association between exposure to higher temperatures and the risk of myocardial infarction at an hourly temporal resolution.Design
Case-crossover study.Setting
England and Wales Myocardial Ischaemia National Audit Project (MINAP) database.Participants
24,861 hospital admissions for myocardial infarction occurring in 11 conurbations during the warmest months (June to August) of the years 2003-09.Main outcome measure
Odds ratio of myocardial infarction for a 1 °C increase in temperature.Results
Strong evidence was found for an effect of heat acting 1-6 hours after exposure to temperatures above an estimated threshold of 20 °C (95% confidence interval 16 °C to 25 °C). For each 1 °C increase in temperature above this threshold, the risk of myocardial infarction increased by 1.9% (0.5% to 3.3%, P=0.009). Later reductions in risk seemed to offset early increases in risk: the cumulative effect of a 1 °C rise in temperature above the threshold was 0.2% (-2.1% to 2.5%) by the end of the third day after exposure.Conclusions
Higher ambient temperatures above a threshold of 20 °C seem to be associated with a transiently increased risk of myocardial infarction 1-6 hours after exposure. Reductions in risk at longer lags are consistent with heat triggering myocardial infarctions early in highly vulnerable people who would otherwise have had a myocardial infarction some time later ("short term displacement"). Policies aimed at reducing the health effects of hot weather should include consideration of effects operating at sub-daily timescales.
SUBMITTER: Bhaskaran K
PROVIDER: S-EPMC3521646 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesTo investigate associations between air pollution levels and myocardial infarction (MI) on short timescales, with data at an hourly temporal resolution.DesignTime stratified case crossover study linking clinical data from the Myocardial Ischaemia National Audit Project (MINAP) with PM(10), ozone, CO, NO(2), and SO(2) data from the UK National Air Quality Archive. Pollution effects were investigated with delays (lags) of 1-6, 7-12, 13-18, 19-24, and 25-72 hours in both single and multi-pollutant models, adjusted for ambient temperature, relative humidity, circulating levels of influenza and respiratory syncytial virus, day of week, holidays, and residual seasonality within calendar month strata.SettingPopulation based study in 15 conurbations in England and Wales.Subjects79,288 diagnoses of myocardial infarction recorded over the period 2003-6.Main outcome measuresExcess risk of myocardial infarction per 10 µg/m(3) increase in pollutant level.ResultsIn single pollutant models, PM(10) and NO(2) levels were associated with a very short term increase in risk of myocardial infarction 1-6 hours later (excess risks 1.2% (95% confidence interval 0.3 to 2.1) and 1.1% (0.3 to 1.8) respectively per 10 μg/m(3) increase); the effects persisted in multi-pollutant models, though with only weak evidence of an independent PM(10) effect (P = 0.05). The immediate risk increases were followed by reductions in risk at longer lags: we found no evidence of any net excess risk associated with the five pollutants studied over a 72 hour period after exposure.ConclusionsHigher levels of PM(10) and NO(2), which are typically markers of traffic related pollution, seem to be associated with transiently increased risk of myocardial infarction 1-6 hours after exposure, but later reductions in risk suggest that air pollution may be associated with bringing events forward in time ("short-term displacement") rather than increasing overall risk. The well established effect of air pollution on cardiorespiratory mortality may not be mediated through increasing the acute risk of myocardial infarction, but through another mechanism.
| S-EPMC3176903 | biostudies-literature
Project description:Peripheral blood samples of patients with acute myocardial infarction were matched with those of control patients to identify possible differences in corresponding gene expression profiles. The controls were matched to cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. Linear models for microarray data (‘limma’) were employed to identify differential gene expression. Shrunken centroids technique helped in identifying the subsets of differentially expressed genes with predictive properties in independent samples. Predictive properties were evaluated using bootstrap sampling. Using the limma modeling with the log-fold change threshold of one (clinical significance) and Storey’s q-value approach (statistical significance), sixty transcripts were found to be both clinically and statistically differentially expressed among the cases not surviving the follow-up period relative to controls, while no such transcripts were observed among other surviving cases. The two subgroups of cases exhibited fourteen differentially expressed transcripts. Predictive modeling indicated sixteen out of sixty transcripts to best discriminate between the controls and cases who died during the follow-up period from cardiovascular causes, while for the surviving cases the already non-significant set of transcripts could not be further reduced. Eleven out of fourteen transcripts were found to best discriminate between the two groups of cases using shrunken centroids. The study identified genes, which were differentially expressed during the acute myocardial infarction, including those associated with short-term fatality of the cases.
2014-12-05 | GSE34198 | GEO
Project description:BackgroundAcute aortic dissection (AAD) is a life-threatening cardiovascular emergency with high mortality, so identifying modifiable risk factors of AAD is of great public health significance. The associations of non-optimal temperature and temperature variability with AAD onset and the disease burden have not been fully understood.MethodsWe conducted a time-stratified case-crossover study using a nationwide registry dataset from 1,868 hospitals in 313 Chinese cities. Conditional logistic regression and distributed lag models were used to investigate associations of temperature and temperature changes between neighboring days (TCN) with the hourly AAD onset and calculate the attributable fractions. We also evaluated the heterogeneity of the associations.FindingsA total of 40,270 eligible AAD cases were included. The exposure-response curves for temperature and TCN with AAD onset risk were both inverse and approximately linear. The risks were present on the concurrent hour (for temperature) or day (for TCN) and lasted for almost 1 day. The cumulative relative risks of AAD were 1.027 and 1.026 per 1°C lower temperature and temperature decline between neighboring days, respectively. The associations were significant during the non-heating period, but were not present during the heating period in cities with central heating. 23.13% of AAD cases nationwide were attributable to low temperature and 1.58% were attributable to temperature decline from the previous day.InterpretationThis is the largest nationwide study demonstrating robust associations of low temperature and temperature decline with AAD onset. We, for the first time, calculated the corresponding disease burden and further showed that central heating may be a modifier for temperature-related AAD risk and burden.FundingThis work was supported by the National Natural Science Foundation of China (92043301 and 92143301), Shanghai International Science and Technology Partnership Project (No. 21230780200), the Medical Research Council-UK (MR/R013349/1), and the Natural Environment Research Council UK (NE/R009384/1).
| S-EPMC9386641 | biostudies-literature
Project description:BACKGROUND: The influence of temperature on acute myocardial infarction (AMI) has not been investigated as extensively as the effects of broader outcomes of morbidity and mortality. Sixteen studies reported inconsistent results and two considered confounding by air pollution. We addressed some of the methodological limitations of the previous studies in this study. METHODS: This is the first study of the association between the daily 3-hour maximum apparent temperature (Tapp(max)) and AMI hospital admissions in Copenhagen. The study period covered 1 January 1999-31 December 2006, stratified in warm (April-September) and cold (October-March) periods. A case-crossover epidemiology study design was applied. Models were adjusted for public holidays and influenza, confounding by PM??, NO? and CO was investigated, the lag and non-linear effects of Tapp(max) was examined, effect modification by age, sex and SES was explored, and the results of the case-crossover models were compared to those of the generalised additive Poisson time-series and generalised estimating equation models. RESULTS: 14,456 AMI hospital admissions (12,995 people) occurred during the study period. For an inter-quartile range (6 or 7°C) increase in the 5-day cumulative average of Tapp(max), a 4% (95% CI:-2%; 10%) and 9% (95% CI: 3%; 14%) decrease in the AMI admission rate was observed in the warm and cold periods, respectively. The 19-65 year old group, men and highest SES group seemed to be more susceptible in the cold period. CONCLUSION: An increase in Tapp(max) is associated with a decrease in AMI admissions during the colder months.
| S-EPMC3353865 | biostudies-other
Project description:Peripheral blood samples of patients with acute myocardial infarction were matched with those of control patients to identify possible differences in corresponding gene expression profiles. The controls were matched to cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. Linear models for microarray data (M-bM-^@M-^XlimmaM-bM-^@M-^Y) were employed to identify differential gene expression. Shrunken centroids technique helped in identifying the subsets of differentially expressed genes with predictive properties in independent samples. Predictive properties were evaluated using bootstrap sampling. Using the limma modeling with the log-fold change threshold of one (clinical significance) and StoreyM-bM-^@M-^Ys q-value approach (statistical significance), sixty transcripts were found to be both clinically and statistically differentially expressed among the cases not surviving the follow-up period relative to controls, while no such transcripts were observed among other surviving cases. The two subgroups of cases exhibited fourteen differentially expressed transcripts. Predictive modeling indicated sixteen out of sixty transcripts to best discriminate between the controls and cases who died during the follow-up period from cardiovascular causes, while for the surviving cases the already non-significant set of transcripts could not be further reduced. Eleven out of fourteen transcripts were found to best discriminate between the two groups of cases using shrunken centroids. The study identified genes, which were differentially expressed during the acute myocardial infarction, including those associated with short-term fatality of the cases. The genome-wide study used matched case-control design, with 97 samples of 90 patients in three disease groups. Matched control samples were used and several (7) technical replicates were performed.
2014-12-05 | E-GEOD-34198 | biostudies-arrayexpress
Project description:ObjectiveTo inform potential pathophysiological mechanisms of air pollution effects on cardiovascular disease (CVD), we investigated short-term associations between ambient air pollution and a range of cardiovascular events from three national databases in England and Wales.MethodsUsing a time-stratified case-crossover design, over 400,000 myocardial infarction (MI) events from the Myocardial Ischaemia National Audit Project (MINAP) database, over 2 million CVD emergency hospital admissions and over 600,000 CVD deaths were linked with daily mean concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter less than 10 μm in aerodynamic diameter (PM10), particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) and sulfur dioxide (SO2), and daily maximum of 8-hourly running mean of O3 measured at the nearest air pollution monitoring site to the place of residence. Pollutant effects were modelled using lags up to 4 days and adjusted for ambient temperature and day of week.ResultsFor mortality, no CVD outcome analysed was clearly associated with any pollutant, except for PM2.5 with arrhythmias, atrial fibrillation and pulmonary embolism. With hospital admissions, only NO2 was associated with a raised risk: CVD 1.7% (95% CI 0.9 to 2.6), non-MI CVD 2.0% (1.1 to 2.9), arrhythmias 2.9% (0.6 to 5.2), atrial fibrillation 2.8% (0.3 to 5.4) and heart failure 4.4% (2.0 to 6.8) for a 10th-90th centile increase. With MINAP, only NO2 was associated with an increased risk of MI, which was specific to non-ST-elevation myocardial infarction (non-STEMIs): 3.6% (95% CI 0.4 to 6.9).ConclusionsThis study found no clear evidence for pollution effects on STEMIs and stroke, which ultimately represent thrombogenic processes, though it did for pulmonary embolism. The strongest associations with air pollution were observed with selected non-MI outcomes.
| S-EPMC4078678 | biostudies-literature
Project description:BACKGROUND:Epidemiological evidence on the health effects of ultrafine particles (UFP) remains insufficient to infer a causal relationship that is largely due to different size ranges and exposure metrics examined across studies. Moreover, evidence regarding the association between UFP and cardiovascular disease at a sub-daily timescale is lacking. OBJECTIVE:We investigated the relationship between different particle metrics, including particle number (PNC), length (PLC), and surface area (PSC) concentrations, and myocardial infarction (MI) at an hourly timescale. METHODS:We collected hourly air pollution and meteorological data from fixed urban background monitoring sites and hourly nonfatal MI cases from a MI registry in Augsburg, Germany, during 2005-2015. We conducted a time-stratified case-crossover analysis with conditional logistic regression to estimate the association between hourly particle metrics and MI cases, adjusted for air temperature and relative humidity. We also examined the independent effects of a certain particle metric in two-pollutant models by adjusting for copollutants, including particulate matter (PM) with an aerodynamic diameter of ?10?m or 2.5?m (PM10 and PM2.5, respectively), nitrogen dioxide, ozone, and black carbon. RESULTS:Overall, a total of 5,898 cases of nonfatal MI cases were recorded. Exploratory analyses showed similar associations across particle metrics in the first 6-12 h. For example, interquartile range increases in PNC within the size range of 10-100?nm, PLC, and PSC were associated with an increase of MI 6 h later by 3.27% [95% confidence interval (CI): 0.27, 6.37], 5.71% (95% CI: 1.79, 9.77), and 5.84% (95% CI: 1.04, 10.87), respectively. Positive, albeit imprecise, associations were observed for PNC within the size range of 10-30?nm and 100-500?nm. Effect estimates for PLC and PSC remained similar after adjustment for PM and gaseous pollutants. CONCLUSIONS:Transient exposure to particle number, length, and surface area concentrations or other potentially related exposures may trigger the onset of nonfatal myocardial infraction. https://doi.org/10.1289/EHP5478.
| S-EPMC7015564 | biostudies-literature
Project description:BackgroundIn studies showing associations between ambient air pollution and myocardial infarction (MI), data have been lacking on the inherent spatial variability of air pollution. The aim of this study was to determine whether the long-term spatial distribution of air pollution influences short-term temporal associations between air pollution and admission to hospital for MI.MethodsWe identified adults living in Calgary who were admitted to hospital for an MI between 2004 and 2012. We evaluated associations between short-term exposure to air pollution (ozone [O3], nitrogen dioxide [NO2], sulfur dioxide [SO2], carbon monoxide [CO], particulate matter < 10 μm in diameter [PM10] and particulate matter < 2.5 μm in diameter [PM2.5]), and hospital admissions for MI using a time-stratified, case-crossover study design. Air Quality Health Index (AQHI) scores were calculated from a composition of O3, NO2 and PM2.5. Conditional logistic regression models were stratified by low, medium and high levels of neighbourhood NO2 concentrations derived from land use regression models; results of these analyses are presented as odds ratios (ORs) with 95% confidence intervals (CIs).ResultsFrom 2004 to 2012, 6142 MIs were recorded in Calgary. Individuals living in neighbourhoods with higher long-term air pollution concentrations were more likely to be admitted to hospital for MI after short-term elevations in air pollution (e.g., 5-day average NO2: OR 1.20, 95% CI 1.03-1.40, per interquartile range [IQR]) as compared with regions with lower air pollution (e.g., 5-day average NO2: OR 0.90, 95% CI 0.78-1.04, per IQR). In high NO2 tertiles, the AQHI score was associated with MI (e.g., 5-day average OR 1.13, 95% CI 1.02-1.24, per IQR; 3-day average OR 1.13, 95% CI 1.04-1.23, per IQR).InterpretationOur results show that the effect of air pollution on hospital admissions for MI was stronger in areas with higher NO2 concentrations than that in areas with lower NO2 concentrations. Individuals living in neighbourhoods with higher traffic-related pollution should be advised of the health risks and be attentive to special air quality warnings.
| S-EPMC7567508 | biostudies-literature
Project description:BackgroundThe primary objective of this study is to estimate the association between body mass index (BMI) and the risk of first acute myocardial infarction (AMI). As a secondary objective, we considered the association between other lifestyle variables, smoking and heavy alcohol use, and AMI risk.MethodsThis study was conducted in the general practice research database (GPRD) which is a database based on general practitioner records and is a representative sample of the United Kingdom population. We matched cases of first AMI as identified by diagnostic codes with up to 10 controls between January 1st, 2001 and December 31st, 2005 using incidence density sampling. We used multiple imputation to account for missing data.ResultsWe identified 19,353 cases of first AMI which were matched on index date, GPRD practice and age to 192,821 controls. There was a modest amount of missing data in the database, and the patients with missing data had different risks than those with recorded values. We adjusted our analysis for each lifestyle variable jointly and also for age, sex, and number of hospitalizations in the past year. Although a record of underweight (BMI <18.0 kg/m2) did not alter the risk for AMI (adjusted odds ratio (OR): 1.00; 95% confidence interval (CI): 0.87-1.11) when compared with normal BMI (18.0-24.9 kg/m2), obesity (BMI > or =30 kg/m2) predicted an increased risk (adjusted OR: 1.41; 95% CI: 1.35-1.47). A history of smoking also predicted an increased risk of AMI (adjusted OR: 1.81; 95% CI: 1.75-1.87) as did heavy alcohol use (adjusted OR: 1.15; 95% CI: 1.06-1.26).ConclusionThis study illustrates that obesity, smoking and heavy alcohol use, as recorded during routine care by a general practitioner, are important predictors of an increased risk of a first AMI. In contrast, low BMI does not increase the risk of a first AMI.
| S-EPMC2241637 | biostudies-literature
Project description:BackgroundPatients with unstable angina (UA) are prone to myocardial infarction (MI) after an attack, yet the altered molecular expression profile therein remains unclear. The current work aims to identify the characteristic hypoxia-related genes associated with UA/MI and to develop a predictive model of hypoxia-related genes for the progression of UA to MI.Methods and resultsGene expression profiles were obtained from the GEO database. Then, differential expression analysis and the WGCNA method were performed to select characteristic genes related to hypoxia. Subsequently, all 10 hypoxia-related genes were screened using the Lasso regression model and a classification model was established. The area under the ROC curve of 1 shows its excellent classification performance and is confirmed on the validation set. In parallel, we construct a nomogram based on these genes, showing the risk of MI in patients with UA. Patients with UA and MI had their immunological status determined using CIBERSORT. These 10 genes were primarily linked to B cells and some inflammatory cells, according to correlation analysis.ConclusionOverall, GWAS identified that the CSTF2F UA/MI risk gene promotes atherosclerosis, which provides the basis for the design of innovative cardiovascular drugs by targeting CSTF2F.
| S-EPMC10351911 | biostudies-literature