Copyright © 2004 by the Johns Hopkins Bloomberg School of Public Health
LETTERS TO THE EDITOR |
THE AUTHORS REPLY
1 German Institute of Human Nutrition, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
2 Harvard School of Public Health, Boston, MA 02115
We thank Dr. Kroke for her comments (1) on the application of reduced rank regression (RRR) in nutritional epidemiology. We agree that using nutrients as response variables in RRR requires both adequate data from food composition tables and prior knowledge of an association between nutrient intake and disease. However, we do not agree that a clear picture of the underlying biologic mechanism is a prerequisite for RRR analysis. For deriving disease-related patterns, the RRR method is already more powerful than principal-components analysis if variation in intake of the selected nutrients is more relevant for disease development than the unspecified variation in intake of all foods. This weaker assumption seems to be fulfilled for the ratio of polyunsaturated fat intake to saturated fat intake, fiber intake, magnesium intake, and alcohol consumption, which were chosen as response variables in our study of diabetes mellitus (2).
The RRR method does not require that the response variables be nutrients or other dietary chemicals. Rather, any continuous variables that are affected by diet and are predictive for the disease are possible responses. For example, biomarkers that are on the causal pathway from diet to disease are predestined for RRR response sets. In a recent application of RRR, five biomarkers of coronary artery disease (high density lipoprotein cholesterol, low density lipoprotein cholesterol, lipoprotein(a), C-peptide, and C-reactive protein) were chosen as response variables (3). The first RRR pattern was strongly associated with the incidence of coronary artery disease (3). A high pattern score corresponded to a biomarker profile of high concentrations of C-reactive protein and C-peptide and low concentrations of high density lipoprotein cholesterol; this profile is a known risk factor for cardiovascular disease. Similarly, in a diabetes study, blood measurements of certain factors, such as glucose, hemoglobin A1c, and C-peptide, could be considered as response variables in an RRR analysis to reflect the overall glycemic response to diet.
The aim of an RRR analysis is not to discover the nutrients or nonnutritive components of a specific food group—for example, fruits and vegetables—that might account for the positive health effects of this food group in observational studies. Rather, starting from the hypotheses that some dietary components or intermediate variables are related to a specific disease, a dietary pattern will be derived by explaining maximal variation in these variables. RRR is limited to studies for which knowledge about important dietary components or intermediate variables exists. In addition, the study of RRR patterns and disease risk should be considered an approach that is complementary to the study of individual nutrients or food components, individual foods, and behavioral dietary patterns. Clearly, it may happen that no appropriate response variables are available at all. In this case—which is an extreme case from the theoretical point of view, regardless of whether or not it is rare—classical principal-components analysis should be the preferred method for obtaining dietary patterns.
REFERENCES
- Kroke A. Re: "Application of a new statistical method to derive dietary patterns in nutritional epidemiology." (Letter). Am J Epidemiol 2004;160:1132.
[Free Full Text] - Hoffmann K, Schulze MB, Schienkiewitz A, et al. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 2004;159:935–44.
[Abstract/Free Full Text] - Hoffmann K, Zyriax BC, Boeing H, et al. A dietary pattern derived to explain biomarker variation is strongly associated with the risk of coronary artery disease. Am J Clin Nutr 2004;80:633–40.
[Abstract/Free Full Text]
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