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Journal article
Application of a new dietary pattern analysis method in nutritional epidemiology
BMC medical research methodology, v 18(1), pp 119-119
29 Oct 2018
PMID: 30373530
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
BackgroundDiet plays an important role in chronic disease, and the use of dietary pattern analysis has grown rapidly as a way of deconstructing the complexity of nutritional intake and its relation to health. Pattern analysis methods, such as principal component analysis (PCA), have been used to investigate various dimensions of diet. Existing analytic methods, however, do not fully utilize the predictive potential of dietary assessment data. In particular, these methods are often suboptimal at predicting clinically important variables.MethodsWe propose a new dietary pattern analysis method using the advanced LASSO (Least Absolute Shrinkage and Selection Operator) model to improve the prediction of disease-related risk factors. Despite the potential advantages of LASSO, this is the first time that the model has been adapted for dietary pattern analysis. Hence, the systematic evaluation of the LASSO model as applied to dietary data and health outcomes is highly innovative and novel. Using Food Frequency Questionnaire data from NHANES 2005-2006, we apply PCA and LASSO to identify dietary patterns related to cardiovascular disease risk factors in healthy US adults (n=2609) after controlling for confounding variables (e.g., age and BMI). Both analyses account for the sampling weights. Model performance in terms of prediction accuracy is evaluated using an independent test set.ResultsPCA yields 10 principal components (PCs) that together account for 65% of the variation in the data set and represent distinct dietary patterns. These PCs are then used as predictors in a regression model to predict cardiovascular disease risk factors. We find that LASSO better predicts levels of triglycerides, LDL cholesterol, HDL cholesterol, and total cholesterol (adjusted R-2=0.861, 0.899, 0.890, and 0.935 respectively) than does the traditional, linear-regression-based, dietary pattern analysis method (adjusted R-2=0.163, 0.005, 0.235, and 0.024 respectively) when the latter is applied to components derived from PCA.ConclusionsThe proposed method is shown to be an appropriate and promising statistical means of deriving dietary patterns predictive of cardiovascular disease risk. Future studies, involving different diseases and risk factors, will be necessary before LASSO's broader usefulness in nutritional epidemiology can be established.
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Details
- Title
- Application of a new dietary pattern analysis method in nutritional epidemiology
- Creators
- Fengqing Zhang - Drexel UniversityTinashe M. Tapera - Drexel UniversityJiangtao Gou - Temple University Health System
- Publication Details
- BMC medical research methodology, v 18(1), pp 119-119
- Publisher
- Springer Nature
- Number of pages
- 10
- Grant note
- Drexel Faculty Summer Research Award Drexel Career Development Award
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000448744200002
- Scopus ID
- 2-s2.0-85055614273
- Other Identifier
- 991019168198304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Health Care Sciences & Services