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Egg Consumption and Risk of Total and Cause-Specific Mortality: An Individual-Based Cohort Study and Pooling Prospective Studies on Behalf of the Lipid and Blood Pressure Meta-analysis Collaboration (LBPMC) Group.
Abstract
The associations of egg consumption with total, coronary heart disease (CHD), and
stroke mortality are poorly understood. We prospectively evaluated the link between
total, CHD, and stroke mortality with egg consumption using a randomly selected sample
of U.S. adults. Next we validated these results within a meta-analysis and systematic
review of all available prospective results. We assessed the mean of cardiometabolic
risk factors across the intake of eggs. We made the analysis based on data from the
National Health and Nutrition Examination Surveys (NHANES; 1999-2010). In NHANES,
vital status through December 31, 2011, was ascertained. Cox proportional hazard regression
models were used to relate baseline egg consumption with all-cause and cause-specific
mortality. PubMed, Scopus, Web of Science, and Google Scholar databases were also
searched (up to December 2017). The DerSimonian-Laird method and generic inverse variance
methods were used for quantitative data synthesis. Overall, 23,524 participants from
NHANES were included (mean age of 47.7 years; 48.7% were men). Across increasing the
intake of eggs, adjusted mean levels of cardiometabolic risk factors worsened. Adjusted
logistic regression showed that participants in the highest category of egg intake
had a greater risk of diabetes (T2DM; 30%) and hypertension (HTN; 48%). With regard
to total and CHD mortality, multivariable Cox regression in a fully adjusted model
showed no link in males and females. In males, egg intake had a reverse (66%) association
with stroke mortality, while this link was not significant among females. The results
of pooling data from published prospective studies also showed no link between CHD
and total mortality with egg consumption, whereas we observed a reverse (28%) association
between egg intake and stroke mortality. These findings were robust after sensitivity
analysis. According to our findings, egg intake had no association with CHD and total
mortality, whereas was associated with lower risk of mortality from stroke. Egg consumption
was associated with T2DM, HTN, C-reactive protein, and markers of glucose/insulin
homeostasis. If confirmed in clinical trials (causation), this information may have
applications for population-wide health measures. Key teaching points No link between
total and CHD mortality with eggs intake in males and females. In males, egg intake
had a reverse association with stroke mortality, while this link was not significant
among females. The results of pooling data from published prospective studies also
showed no link between CHD and total mortality with egg consumption, whereas we observed
a reverse association between egg intake and stroke mortality.
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Journal articlePermalink
https://hdl.handle.net/10161/18954Published Version (Please cite this version)
10.1080/07315724.2018.1534620Publication Info
Mazidi, Mohsen; Katsiki, Niki; Mikhailidis, Dimitri P; Pencina, Michael J; & Banach,
Maciej (2019). Egg Consumption and Risk of Total and Cause-Specific Mortality: An Individual-Based
Cohort Study and Pooling Prospective Studies on Behalf of the Lipid and Blood Pressure
Meta-analysis Collaboration (LBPMC) Group. Journal of the American College of Nutrition. pp. 1-12. 10.1080/07315724.2018.1534620. Retrieved from https://hdl.handle.net/10161/18954.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Michael J Pencina
Professor of Biostatistics & Bioinformatics
Michael J. Pencina, PhD Chief Data Scientist, Duke Health Vice Dean for Data Science
Director, Duke AI Health Professor, Biostatistics & Bioinformatics Duke University
School of Medicine
Michael J. Pencina, PhD, is Duke Health's chief data scientist and serves as vice
dean for data science, director of Duke AI Health, and professor of biostatistics
and bioinformatics at the Duke University School of Medicine. His work bridges the
fiel

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