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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 160520, 14 pages
Research Article

Structural Equation Modeling for Analyzing Erythrocyte Fatty Acids in Framingham

1Health Diagnostic Laboratory Inc., Richmond, VA 23219, USA
2Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57105, USA
3Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57007, USA
4Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
5Department of Statistics, University of Akron, Akron, OH 44325, USA
6Department of Biostatistics, Boston University School of Public Health, Boston, MA 02218, USA
7Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
8Framingham Heart Study, Framingham, MA 01702, USA
9OmegaQuant Analytics, Sioux Falls, SD 57107, USA

Received 26 December 2013; Revised 28 February 2014; Accepted 28 February 2014; Published 15 April 2014

Academic Editor: Zhenyu Jia

Copyright © 2014 James V. Pottala et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Research has shown that several types of erythrocyte fatty acids (i.e., omega-3, omega-6, and trans) are associated with risk for cardiovascular diseases. However, there are complex metabolic and dietary relations among fatty acids, which induce correlations that are typically ignored when using them as risk predictors. A latent variable approach could summarize these complex relations into a few latent variable scores for use in statistical models. Twenty-two red blood cell (RBC) fatty acids were measured in Framingham (N = 3196). The correlation matrix of the fatty acids was modeled using structural equation modeling; the model was tested for goodness-of-fit and gender invariance. Thirteen fatty acids were summarized by three latent variables, and gender invariance was rejected so separate models were developed for men and women. A score was developed for the polyunsaturated fatty acid (PUFA) latent variable, which explained about 30% of the variance in the data. The PUFA score included loadings in opposing directions among three omega-3 and three omega-6 fatty acids, and incorporated the biosynthetic and dietary relations among them. Whether the PUFA factor score can improve the performance of risk prediction in cardiovascular diseases remains to be tested.