Lifestyle variables and the risk of myocardial infarction in the General Practice Research Database

被引：26

作者：

Delaney J.A.C. ^{[1
,2
]}

Daskalopoulou S.S. ^{[2
,5
]}

Brophy J.M. ^{[1
,2
,3
]}

Steele R.J. ^{[4
]}

Opatrny L. ^{[2
,5
]}

Suissa S. ^{[1
,2
]}

机构：

[1] Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, ON

[2] Division of Clinical Epidemiology, McGill University Health Center, Montreal, ON

[3] Division of Cardiology, Royal Victoria Hospital, McGill University Health Center, Montreal, QC

[4] Department of Mathematics and Statistics, McGill University, Montreal, ON

[5] Division of Internal Medicine, McGill University Health Center, Montreal, ON

来源：

BMC Cardiovascular Disorders | / 7卷 / 1期

关键词：

Body Mass Index; Acute Myocardial Infarction; Multiple Imputation; Normal Body Mass Index; Body Mass Index Category;

D O I：

10.1186/1471-2261-7-38

中图分类号：

学科分类号：

摘要：

Background: The 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. Methods: This 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. Results: We 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 ≥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). Conclusion: This 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. © 2007 Delaney et al; licensee BioMed Central Ltd.

引用

共 30 条

[1]

De Bacquer D., De Backer G., Cokkinos D., Keil U., Montaye M., Ostor E., Pyorala K., Sans S., Overweight and obesity in patients with established coronary heart disease: Are we meeting the challenge?, Eur Heart J, 25, pp. 121-128, (2004)

[2]

Mulnier H.E., Seaman H.E., Raleigh V.S., Soedamah-Muthu S.S., Colhoun H.M., Lawrenson R.A., Mortality in people with type 2 diabetes in the UK, Diabet Med, 23, pp. 516-521, (2006)

[3]

Andersohn F., Suissa S., Garbe E., Use of first- and second-generation cyclooxygenase-2-selective nonsteroidal antiinflammatory drugs and risk of acute myocardial infarction, Circulation, 113, pp. 1950-1957, (2006)

[4]

van der Heijden G.J., Donders A.R., Stijnen T., Moons K.G., Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: A clinical example, J Clin Epidemiol, 59, pp. 1102-1109, (2006)

[5]

Barzi F., Woodward M., Imputations of missing values in practice: Results from imputations of serum cholesterol in 28 cohort studies, Am J Epidemiol, 160, pp. 34-45, (2004)

[6]

Greenland S., Finkle W.D., A critical look at methods for handling missing covariates in epidemiologic regression analyses, Am J Epidemiol, 142, pp. 1255-1264, (1995)

[7]

Lawrenson R., Williams T., Farmer R., Clinical information for research

[8]

the use of general practice databases, J Public Health Med, 21, pp. 299-304, (1999)

[9]

Jick S.S., Kaye J.A., Vasilakis-Scaramozza C., Garcia Rodriguez L.A., Ruigomez A., Meier C.R., Schlienger R.G., Black C., Jick H., Validity of the general practice research database, Pharmacotherapy, 23, pp. 686-689, (2003)

[10]

Gorelick M.H., Bias arising from missing data in predictive models, J Clin Epidemiol, 59, pp. 1115-1123, (2006)

← 1 2 3 →