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.

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2019-06-07

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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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10.1080/07315724.2018.1534620

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Mazidi, Mohsen, Niki Katsiki, Dimitri P Mikhailidis, Michael J Pencina and Maciej Banach (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.

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Scholars@Duke

Pencina

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 fields of data science, health care, and AI, contributing to Duke’s national leadership in trustworthy health AI.

Dr. Pencina partners with key leaders to develop data science strategies for Duke Health that span and connect academic research and clinical care. As vice dean for data science, he develops and implements quantitative science strategies to support the School of Medicine’s missions in education and training, laboratory and clinical science, and data science.

He co-founded and co-leads the national Coalition for Health AI (CHAI), a multi-stakeholder effort whose mission is to increase trustworthiness of AI by developing guidelines to drive high-quality health care through the adoption of credible, fair, and transparent health AI systems. He also spearheaded the establishment and co-chairs Duke Health’s Algorithm-Based Clinical Decision Support (ABCDS) Oversight Committee and serves as co-director of Duke’s Collaborative to Advance Clinical Health Equity (CACHE).

Dr. Pencina is an internationally recognized authority in the evaluation of AI algorithms. Guideline groups rely on his work to advance best practices for the application of clinical decision support tools in health delivery. He interacts frequently with investigators from academic and industry institutions as well as government officials. Since 2014, he has been acknowledged annually by Thomson Reuters/Clarivate Analytics as one of the world’s "highly cited researchers" in clinical medicine and social sciences, with over 400 publications cited over 100,000 times. He serves as a deputy editor for statistics at JAMA-Cardiology.

Dr. Pencina joined the Duke University faculty in 2013, and served as director of biostatistics for the Duke Clinical Research Institute until 2018. Previously, he was an associate professor in the Department of Biostatistics at Boston University and the Framingham Heart Study, and director of statistical consulting at the Harvard Clinical Research Institute. He received his PhD in Mathematics and Statistics from Boston University in 2003 and holds master’s degrees from the University of Warsaw in actuarial mathematics and business culture.

Email: michael.pencina@duke.edu

Web Sites:  medschool.duke.edu; aihealth.duke.edu; https://scholars.duke.edu/person/michael.pencina

Phone:  919.613.9066

Address:  Duke University School of Medicine; 2424 Erwin Road, Suite 903; Durham, NC 27705

 


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