Familial Clustering for Features of the Metabolic Syndrome

The National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study

  1. Weihong Tang, MD, PHD1,
  2. Yuling Hong, MD, PHD2,
  3. Michael A. Province, PHD3,
  4. Stephen S. Rich, PHD4,
  5. Paul N. Hopkins, MD, MSPH5,
  6. Donna K. Arnett, PHD6,
  7. James S. Pankow, PHD1,
  8. Michael B. Miller, PHD1 and
  9. John H. Eckfeldt, MD7
  1. 1Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
  2. 2American Heart Association National Center, Dallas, Texas
  3. 3Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
  4. 4Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina
  5. 5Cardiovascular Genetics, University of Utah School of Medicine, Salt Lake City, Utah
  6. 6Department of Epidemiology, University of Alabama-Birmingham, Birmingham, Alabama
  7. 7Department of Laboratory Medicine and Pathology, Medical School, University of Minnesota, Minneapolis, Minnesota
  1. Address correspondence and reprint requests to Weihong Tang, MD, PhD, Division of Epidemiology and Community Health, University of Minnesota, 1300 South Second St., Suite 300, Minneapolis, MN 55454. E-mail: tang0097{at}


OBJECTIVE—Metabolic syndrome–related traits (obesity, glucose intolerance/insulin resistance, dyslipidemia, and hypertension) have been shown to be genetically correlated. It is less clear, however, if the genetic correlation extends to novel risk factors associated with inflammation, impaired fibrinolytic activity, and hyperuricemia. We present a bivariate genetic analysis of MetS-related traits including both traditional and novel risk factors.

RESEARCH DESIGN AND METHODS—Genetic correlations were estimated using a variance components procedure in 1,940 nondiabetic white individuals from 445 families in the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study. Twelve MetS-related traits, including BMI, waist circumference, blood pressure, white blood cell count, fasting serum triglycerides, HDL cholesterol, insulin, glucose, plasminogen activator inhibitor-1 antigen, uric acid, and C-reactive protein, were measured and adjusted for covariates, including lifestyle variables.

RESULTS—Significant genetic correlations were detected among BMI, waist circumference, HDL cholesterol, triglycerides, insulin, and plasminogen activator inhibitor-1 antigen and between uric acid and all of the above variables except insulin. C-reactive protein and white blood cell count were genetically correlated with each other, and both showed significant genetic correlations with waist circumference and insulin. Fasting glucose was not significantly genetically correlated with any of the other traits.

CONCLUSIONS—These results suggest that pleiotropic effects of genes or shared family environment contribute to the familial clustering of MetS-related traits.


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    • Accepted December 6, 2005.
    • Received April 19, 2005.
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