• Transl Res · Feb 2008

    Bivariate mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African Americans, Hispanics, and whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study.

    • Christine E McLaren, Victor R Gordeuk, Wen-Pin Chen, James C Barton, Ronald T Acton, Mark Speechley, Oswaldo Castro, Paul C Adams, Beverly M Snively, Emily L Harris, David M Reboussin, Geoffrey J McLachlan, Richard Bean, and Hemochromatosis and Iron Overload Screening Study Research Investigators.
    • Department of Epidemiology, University of California, Irvine, California, USA. cmclaren@uci.edu
    • Transl Res. 2008 Feb 1; 151 (2): 9710997-109.

    AbstractBivariate mixture modeling was used to analyze joint population distributions of transferrin saturation (TS) and serum ferritin concentration (SF) measured in the Hemochromatosis and Iron Overload Screening (HEIRS) Study. Four components (C1, C2, C3, and C4) with successively age-adjusted increasing means for TS and SF were identified in data from 26,832 African Americans, 12,620 Asians, 12,264 Hispanics, and 43,254 whites. The largest component, C2, had normal mean TS (21% to 26% for women, 29% to 30% for men) and SF (43-82 microg/L for women, 165-242 microg/L for men), which consisted of component proportions greater than 0.59 for women and greater than 0.68 for men. C3 and C4 had progressively greater mean values for TS and SF with progressively lesser component proportions. C1 had mean TS values less than 16% for women (<20% for men) and SF values less than 28 microg/L for women (<47 microg/L for men). Compared with C2, adjusted odds of iron deficiency were significantly greater in C1 (14.9-47.5 for women, 60.6-3530 for men), adjusted odds of liver disease were significantly greater in C3 and C4 for African-American women and all men, and adjusted odds of any HFE mutation were increased in C3 (1.4-1.8 for women, 1.2-1.9 for men) and in C4 for Hispanic and white women (1.5 and 5.2, respectively) and men (2.8 and 4.7, respectively). Joint mixture modeling identifies a component with lesser SF and TS at risk for iron deficiency and 2 components with greater SF and TS at risk for liver disease or HFE mutations. This approach can identify populations in which hereditary or acquired factors influence metabolism measurement.

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