An Overview of the Genomics of Metabolic Syndrome

Authors

  • Jacquelyn Y. Taylor PhD, PNP-BC, RN, FAAN,

    1. Delta Mu, Associate Professor and Robert Wood Johnson Foundation Nurse Faculty Scholar, School of Nursing, Yale University, New Haven, CT
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  • Aldi T. Kraja DSc, PhD,

    1. Research Associate Professor of Genetics, Division of Statistical Genomics, Center of Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO
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  • Lisa de las Fuentes MD, MS,

    1. Assistant Professor of Medicine, School of Medicine, Division of Cardiovascular Diseases, Department of Internal Medicine Washington University in St. Louis, MO
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  • Ansley Grimes Stanfill BSN, RN,

    1. Beta Theta-at-large, Doctoral Student, College of Nursing, University of Tennessee Health Science Center, Memphis, TN
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  • Ashley Clark MSN, RN,

    1. Delta Mu, Doctoral Student, School of Nursing, Yale University, New Haven, CT
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  • Ann Cashion PhD, RN, FAAN

    1. Beta Theta-at-large, Professor and Chair of the Department of Acute and Chronic Care, College of Nursing, University of Tennessee Health Science Center, Memphis, TN
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Dr. Jacquelyn Taylor, 100 Church Street South, Room 295, New Haven, CT 06536. E-mail: jacquelyn.taylor@yale.edu

Abstract

Purpose: This article provides a brief overview of the diagnostic criteria and genomic risk factors for the components of metabolic syndrome (MetS).

Organizing Constructs: Contributions of cardiovascular, obesity, and diabetes genomic risk factors to the development of MetS as reported in the literature have been reviewed.

Findings: The genomic risk factors for the development of MetS are strongly linked to the genomic risk factors that make up the components of the disease. Many of the cardiovascular and renal genomic risk factors for MetS development are similar to those found in the development of hypertension and dyslipidemia. Obesity may act as a master trigger to turn on the gene expression changes necessary for the other components of the disease. Studies in the genomics of type 2 diabetes show a number of overlapping genes and polymorphisms that influence both the development of diabetes and MetS.

Conclusions: Although health practitioners now have some insights into the genomics of risk factors associated with MetS, the overall understanding of MetS remains inadequate. Clinical applications based on some of the discussed genomic risk factors are being developed but are not yet available for the diagnosis and treatment of MetS.

Clinical Relevance: A broad knowledge of the genomic contributions to disease processes will enable the clinician to better utilize genomics to assess and tailor management of patients.

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