A case-control study of physical activity patterns and risk of non-fatal myocardial infarction

Jian Gong 0 Hannia Campos 3 Mark Joseph A Fiecas 2 Stephen T McGarvey 0 Robert Goldberg 5 Caroline Richardson 4 Ana Baylin 0 1 0 Department of Community Health, Brown University , Providence, RI 02912 , USA 1 Department of Epidemiology, School of Public Health, University of Michigan , Ann Arbor, MI 48109 , USA 2 Department of Psychiatry, University of California , San DiegoLa JollaCA 92093 , USA 3 Department of Nutrition, Harvard School of Public Health , Boston, MA 02115 , USA 4 Deparment of Family Medicine, University of Michigan , Ann Arbor, MI 48109 , USA 5 Department of Quantitative Health Sciences, University of Massachusetts Medical School , Worcester, MA 01655 , USA Background: The interactive effects of different types of physical activity on cardiovascular disease (CVD) risk have not been fully considered in previous studies. We aimed to identify physical activity patterns that take into account combinations of physical activities and examine the association between derived physical activity patterns and risk of acute myocardial infarction (AMI). Methods: We examined the relationship between physical activity patterns, identified by principal component analysis (PCA), and AMI risk in a case-control study of myocardial infarction in Costa Rica (N=4172), 1994-2004. The component scores derived from PCA and total METS were used in natural cubic spline models to assess the association between physical activity and AMI risk. Results: Four physical activity patterns were retained from PCA that were characterized as the rest/sleep, agricultural job, light indoor activity, and manual labor job patterns. The light indoor activity and rest/sleep patterns showed an inverse linear relation (P for linearity=0.001) and a U-shaped association (P for non-linearity=0.03) with AMI risk, respectively. There was an inverse association between total activity-related energy expenditure and AMI risk but it reached a plateau at high levels of physical activity (P for non-linearity=0.01). Conclusions: These data suggest that a light indoor activity pattern is associated with reduced AMI risk. PCA provides a new approach to investigate the relationship between physical activity and CVD risk. - Background Numerous observational epidemiologic studies have demonstrated that physical activity is inversely related to cardiovascular morbidity and mortality [1-4]. Physical activity may contribute up to 20% - 30% reduced risk of coronary heart disease [5,6]. However, studies have shown that different types of physical activities may have different effects on the risk of cardiovascular disease (CVD) and may interact together [7-12]. For example, some leisure time activities such as walking, stair climbing, and cycling provide protection against CVD [7-12], whereas others, such as intensive domestic physical activity, may not offer protection against CVD [11]. There are also interactive effects between lack of exercise and sitting at work and between demanding household work and sitting at work on the association with increased risk of acute myocardial infarction (AMI) [9]. Therefore, if we use a single summary measurement to reflect physical activity, such as METS, the association between physical activity and risk of CVD might be biased because subjects who have the same measured value may have a distinct combination of physical activities. Furthermore, studying different types of physical activity in isolation may not adequately consider any joint and interactive associations on the risk of CVD. Previous models that incorporate one type of physical activity of interest and other types of physical activity (as potential confounders) for exploring the effects of each type of physical activity on CVD may be problematic because of the concomitant change in total physical activity. As one type of physical activity increases, total physical activity increases as well, given that the other physical activities are fixed. Hence, the effect estimate of one type of physical activity does not present its pure effect, but includes the effects of total physical activity. In order to overcome these challenges in the analysis of physical activity data, we used the method of principal component analysis (PCA) [13] to identify physical activity patterns that take into account combinations of physical activities. We used both parametric and semi-parametric regression models to examine the association between derived physical activity patterns and risk of acute myocardial infarction (AMI). Data from a population-based, casecontrol study in Costa Rica were utilized for purposes of this investigation. Methods Study population In Costa Rica, CVD has been the countrys leading cause of death since 1970 and the mortality rate for CVD has been declining since 2002 according to 2007 Health in the Americas, a report from World Health Organization. The participants in this study are cases and controls from a case-control study of non-fatal myocardial infarction conducted in the Central Valley in Costa Rica from 1994 to 2004. The study design and population have been described previously [14,15]. In brief, eligible cases were men and women who were diagnosed as survivors of a first AMI by two independent cardiologists at any of the six recruiting hospitals in the Central Valley of Costa Rica during the period 1994-2004. All cases met the World Health Organization criteria for AMI [16]. Enrollment was carried out while cases were in the hospitals step-down-unit. One free-living control subject for each case, matched for age ( 5 years), sex, and area of residence (county), was randomly selected using information available at the National Census and Statistics Bureau of Costa Rica. Participation rates were 98% for cases and 88% for controls. Cases and controls provided informed consent on documents approved by the Human Subjects Committee of the Harvard School of Public Health and the University of Costa Rica. Data collection Trained interviewers visited all study participants at their homes for purposes of collecting sociodemographic characteristics, physical activity, lifestyle, medical history, smoking, and dietary data by use of a standardized questionnaire [15]. They visited cases, on average, within 3 weeks of hospital discharge (for controls, hospital discharge of the corresponding case subject) and when possible, by the same interviewer. Identical questionnaires and data collection procedures were used for cases and controls. The standardized activity questionnaire consisted of 18 questions and physical activity was determined by asking subjects the average frequency and time spent on several occupational and leisure time activities during the last year. These activities were grouped into six categories according to their intensity or metabolic equivalents (METs): lying quietly in bed: afternoon nap or rest and night sleep (0.9 METs); sitting (1.0 METs); light indoor activity such as standing at work or at (...truncated)