ArticleComputational and mathematical methods in medicine2014
Structural equation modeling for analyzing erythrocyte fatty acids in Framingham.
Article in Computational and mathematical methods in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Red Blood Cell Polyunsaturated Fatty Acids and Mortality Following Breast Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Trial
- Patterns of adipose tissue fatty acids and the risk of atrial fibrillation: A case-cohort study.PloS one · 2018Article
- Associations of erythrocyte fatty acid patterns with insulin resistance.The American journal of clinical nutrition · 2016Article
- Red Blood Cell Fatty Acids and Incident Diabetes Mellitus in the Women's Health Initiative Memory Study.PloS one · 2016Observational
- Red blood cell polyunsaturated fatty acids and mortality in the Women's Health Initiative Memory Study.Journal of clinical lipidologyArticle
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
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