American Journal of Epidemiology Advance Access originally published online on October 22, 2008
American Journal of Epidemiology 2008 168(12):1433-1443; doi:10.1093/aje/kwn274
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PRACTICE OF EPIDEMIOLOGY |
Comparison of 3 Methods for Identifying Dietary Patterns Associated With Risk of Disease
Correspondence to Dr. Ana Baylin, Department of Community Health, Brown University, Box G-S121, 121 South Main Street, 2nd Floor, Providence, RI 02903 (e-mail: ana_baylin{at}brown.edu).
Received for publication April 3, 2008. Accepted for publication July 31, 2008.
Reduced rank regression and partial least-squares regression (PLS) are proposed alternatives to principal component analysis (PCA). Using all 3 methods, the authors derived dietary patterns in Costa Rican data collected on 3,574 cases and controls in 1994–2004 and related the resulting patterns to risk of first incident myocardial infarction. Four dietary patterns associated with myocardial infarction were identified. Factor 1, characterized by high intakes of lean chicken, vegetables, fruit, and polyunsaturated oil, was generated by all 3 dietary pattern methods and was associated with a significantly decreased adjusted risk of myocardial infarction (28%–46%, depending on the method used). PCA and PLS also each yielded a pattern associated with a significantly decreased risk of myocardial infarction (31% and 23%, respectively); this pattern was characterized by moderate intake of alcohol and polyunsaturated oil and low intake of high-fat dairy products. The fourth factor derived from PCA was significantly associated with a 38% increased risk of myocardial infarction and was characterized by high intakes of coffee and palm oil. Contrary to previous studies, the authors found PCA and PLS to produce more patterns associated with cardiovascular disease than reduced rank regression. The most effective method for deriving dietary patterns related to disease may vary depending on the study goals.
cardiovascular diseases; diet; myocardial infarction; principal component analysis
Abbreviations: PCA, principal component analysis; PLS, partial least-squares regression; PRESS, predicted residual sum of squares; RRR, reduced rank regression
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