|
|||||||||
|
|
|
|
|
|
|
|
|
|
|
CLINICAL OBSERVATIONS, INTERVENTIONS, AND THERAPEUTIC TRIALS
From the Divisions of Epidemiology and
Hematology/Oncology, University of California, Irvine, College of
Medicine, Irvine; Biostatistics Shared Resource, Chao Family
Comprehensive Cancer Center, University of California, Irvine; Center
for Sickle Cell Disease, Howard University, Washington, DC; Center for
Health Policy Research and Education, Duke University, Durham, NC; and
Veterans Affairs Long Beach Health Care System, Long Beach, CA.
In previous analyses of transferrin saturation data in African
Americans and Caucasians from the second National Health and Nutrition
Examination Survey (NHANES II), subpopulations were found consistent
with population genetics for common loci that influence iron
metabolism. The goal of this new study was to determine if these
transferrin saturation subpopulations have different levels of iron
stores. Statistical mixture modeling was applied to transferrin
saturation data for African Americans and Caucasians from the third
National Health and Nutrition Examination Survey (NHANES III), and then
the mean serum ferritin concentrations were determined for the
transferrin saturation subpopulations that were identified. After
adjustment for diurnal variation, 3 subpopulations of transferrin
saturation were identified in each racial group. Satisfying
Hardy-Weinberg conditions for major locus effects, in both racial
groups the sum of the square roots of the proportion with the lowest
mean transferrin saturation and the proportion with the highest mean
transferrin saturation was approximately 1. When weighted to reflect
the US adult population as a whole, these subpopulations of increasing
transferrin saturations had progressively increasing mean age-adjusted
serum ferritin concentration values in each ethnic grouping as
stratified by sex (trend test, P < .002 for all). These
results are consistent with the concept that population transferrin
saturation subpopulations reflect different levels of storage iron.
(Blood. 2001;98:2345-2351) We previously used mixture modeling of transferrin
saturations measured in the second National Health and Nutrition
Examination Survey (NHANES II) to study possible genetic influences on
iron metabolism in the African American population1 and the
Caucasian population2 of the United States. In the study of
NHANES II Caucasian data, we analyzed the distribution of transferrin
saturation after removing values for individuals possibly homozygous
for the gene responsible for hemochromatosis and found evidence for 2 subpopulations, one postulated to be predominantly unaffected by the
gene for hemochromatosis and the other predominantly heterozygous for
hemochromatosis.2 More recently, we examined NHANES II data from African Americans from which markedly elevated transferrin saturation values were not removed, and mixture modeling provided evidence for 3 subpopulations of transferrin saturation. Although the
type of hemochromatosis found in Caucasians may be rare in African
Americans, our findings for African Americans were consistent with
population genetics for a common locus that influences iron metabolism.1
Transferrin saturation can be reduced with inflammation as well as iron
deficiency3 and the same measurement can be elevated by
increased iron stores and a variety of other conditions.4 Therefore, it is not completely clear that, on a population basis, elevated transferrin saturation reflects increased iron stores. In
fact, in a recent analysis of data from the third National Health and
Nutrition Examination Survey (NHANES III), only 11% to 22% of adults
with elevated serum transferrin saturation as defined by various cutoff
values had a concurrently elevated serum ferritin level.5
In the present study, we analyzed data from the NHANES III to determine
if subpopulations based on transferrin saturation have different levels
of storage iron as assessed by measurements of serum ferritin concentration.
Sources of data
Serum iron concentration and total iron-binding capacity were measured
colorimetrically by using an Alpkem RFA analyzer (Alpkem, Clackamas,
OR); a 1% thiourea solution was added to complex Cu++ to
prevent copper interference. Serum transferrin saturation was
calculated by dividing the serum iron level by total iron-binding capacity and multiplying by 100. Information regarding methodologic variation over the assays was provided by Gunter and
colleagues.7 The coefficients of variation for analyses of
control pools averaged 3.1% and 3.3% for serum iron and total
iron-binding capacity, respectively. Serum ferritin concentration was
measured by using the Quantimmune IRMA kit (Biorad Laboratories,
Hercules, CA). The coefficient of variation for serum ferritin control
pools covering a range of medium to high concentrations (approximately 25-440 ng/mL) was 5.4% on average, and for low-concentration pools (5-10 ng/mL) it was 10.5%.7
Selection criteria
Adjustment of transferrin saturations for diurnal variation Because transferrin saturation has a diurnal variation,12,20 including samples obtained at different times of the day, without appropriate adjustment, might alter the distribution of transferrin saturation. To address this possibility, regression methods were used to adjust transferrin saturation values for blood samples drawn in the afternoon or evening to the predicted values for blood samples drawn in the morning in the same individuals.1 For each data set, transferrin saturation values were divided into deciles and a regression equation was determined to predict the adjusted average value for morning transferrin saturation for each decile. For blood samples drawn in the afternoon or evening, the predicted average transferrin saturation value was computed. The final race- and gender-specific data sets consisted of the actual transferrin saturation values from samples drawn in the morning and the predicted values for samples drawn in the afternoon or evening.Adjustment of the data from African Americans to account for a possible admixture of Caucasian hereditary hemochromatosis genes The data were modified to allow for the possibility that the distribution of transferrin saturations among African Americans is affected by individuals who are heterozygotes or homozygotes for hereditary hemochromatosis. The gene frequency for the hereditary hemochromatosis locus in the Caucasian population is estimated to be 0.067.4 Assuming a 25% admixture of Caucasian genes in the African American population,21 the gene frequency for the hereditary hemochromatosis locus in African Americans would be 25% of 0.067 or 0.017. The actual frequency for the Cys282Tyr mutation among African Americans was somewhat lower in 2 geographic areas of the United States, a reported 0.011 in San Diego, California22 and 0.007 in Jefferson County, Alabama.23 Assuming a nationwide gene frequency of 0.017, population genetics would then project a proportion of homozygotes for the hereditary hemochromatosis gene of 3/10 000 (0.0172) and a proportion of heterozygotes of 330/10 000. We assumed that for African Americans, the transferrin saturation values from heterozygotes for the hereditary hemochromatosis locus would be normally distributed with the same means and SDs as found in our previous study of Caucasian Americans in NHANES II.2 Under this assumption, to adjust for hemochromatosis heterozygosity, we removed 3.3% of the 1301 values from the data set for men (n = 43) that were closest to 43 random values generated from a normal distribution with a mean of 46.5% and an SD of 7%. Similarly, we removed 3.3% of the 1089 values from the data set for women (n = 36) that were closest to 36 random values generated from a normal distribution with a mean of 43.4% and an SD of 7%. In the case when several transferrin saturation values were equidistant from a randomly generated value, one of these values was selected randomly. Because the number of transferrin saturations arising from homozygotes for hereditary hemochromatosis was projected to be less than one for men and women, no further adjustments for possible admixture were made to the data sets.Statistical modeling of transferrin saturations Data sets for transferrin saturation modeling consisted of 1258 African American men, 1053 African American women, 2328 Caucasian men, and 2483 Caucasian women (Table 1). As described previously, mixture distribution modeling was applied to determine the best fit of each grouped frequency distribution1,2,24 using the DISFIT computer program.25 In brief, transferrin saturation values were sorted into intervals of 2% and the frequency of values within each interval was computed. The physiologic models we considered were a single normal distribution, a mixture of 2 normal distributions, and a mixture of 3 normal distributions. We have previously established that transferrin saturations in a homogeneous population follow a normal distribution.2 The expectation-maximization algorithm was applied to the distributions of transferrin saturation values for parameter estimation.26,27 The statistical test used to determine the best fitting model was based on the likelihood ratio statistic. Using a hierarchical structure to analyze each observed distribution, the maximized log-likelihood function for a mixture of 3 normal distributions was evaluated (Log L3) and compared with the maximized log-likelihood function for 2 normal distributions (Log L2) and for single normal distribution (Log L1). Significance of the likelihood ratio statistics [ 2Log
(L3/L2)] and [2Log (L3/L1)] at the 0.01 level was
assessed using a resampling technique.28 For the
3-population model, the methods of Crump and Howe were used to compute
confidence intervals for proportions.29
Estimation of gene frequency for possible loci that influence iron status Accepting the possibility that our findings might represent the presence of loci that influence iron metabolism, we assumed that the 3 normal distributions of transferrin saturation in our analyses would represent predominantly a subpopulation of normal homozygotes, a subpopulation of heterozygotes, and a subpopulation of affected homozygotes. We estimated the proportions of normal homozygotes, heterozygotes, and affected homozygotes as the proportions in the populations with the lowest, intermediate, and highest mean transferrin saturations, respectively. According to the Hardy-Weinberg equilibrium equation, p2 (the proportion of normal homozygotes) + 2pq (the proportion of heterozygotes) + q2 (the proportion of abnormal homozygotes) = 1. We estimated the gene frequency of the abnormal allele (q) as the square root of the proportion from the population with the highest mean transferrin saturation. We estimated the gene frequency of the normal homozygotes (p) as the square root of the proportion from the population with the lowest mean transferrin saturation. We then examined whether the model distributions were consistent with population genetics for a major locus effect in which p + q = 1.The means for transferrin saturation within corresponding subpopulations were compared between African Americans and Caucasians of the same gender using the independent sample t test. Comparison of mean serum ferritin concentrations within subpopulations determined on the basis of transferrin saturation A goal of our study was to determine if subpopulations of individuals, determined on the basis of transferrin saturation, have significantly different mean iron stores. Considering the transferrin saturation data sets used for mixture modeling, 26 subjects did not have measurements of serum ferritin concentration. After removal of values for these individuals, the data sets for analysis of serum ferritin concentration consisted of 1252 African American men, 1047 African American women, 2322 Caucasian men, and 2475 Caucasian women. Because of the marked skewness in the distributions of serum ferritin concentration, the square root transformation was applied to each data set. Because serum ferritin concentration tends to increase with age,30-37 we developed a method for age standardization based on statistical regression analysis. Linear, piece-wise linear, and nonlinear regression models, stratified by race and gender, were examined by regressing the square root of the observed serum ferritin concentration on the age at interview. The best fitting models showed an increase to middle age followed by steady state or decline in serum ferritin. Because of the lack of a biologic explanation for age-related decline in serum ferritin in terms of reduction of iron stores, we adopted a conditional approach to the modeling. For individuals below 60 years of age we used race- and gender-specific linear regression equations to calculate the predicted serum ferritin at age 60. For individuals 60 years of age or older, the observed serum ferritin was used in further analyses.The probability that an individual serum ferritin concentration value belonged to one of the 3 transferrin saturation subpopulations was computed. Given the total sample size, the number of expected observations within each transferrin saturation interval was calculated. Within a given transferrin saturation interval, each observation was then assigned a probability by random assignment from a uniform (0,1) distribution and these probabilities were used to assign individual serum ferritin concentration values to a given subgroup according to the expected proportions within transferrin saturation subpopulations. Two hundred repetitions of the assignment of serum ferritin concentration values to subpopulations were made and the average mean and variance for serum ferritin concentration within subpopulations were computed. The parametric trend test38 was used to compare the mean square root of serum ferritin concentration within each of 3 transferrin saturation subgroups, taking into account the unequal sample sizes for subgroups of subjects. Weighting the results to reflect the US population as a whole The assumptions underlying our analysis were that transferrin saturation values are independent and identically distributed; that is, each observation has an equal chance of being selected and that all observations come from the same distribution. However, because individuals in the NHANES III sample did not have an equal probability of selection, sample weights must be used to calculate parameter estimates that reflect the US population. For NHANES III, a multistage estimation procedure was used to calculate sample weights so that point estimates would reflect the US population. The sampling weights were based on the March 1990 and March 1993 Current Population Survey, adjusted for undercounts, for the civilian noninstitutionalized US population.39 The methods described above were used to compute parameter estimates from the weighted transferrin saturation distributions to reflect results for African American and Caucasian men and women in the US population fitting our exclusion criteria. It was not possible to adjust the variance estimates to account for the complex design of NHANES III.
Analysis of unweighted transferrin saturation data The primary analysis was performed on transferrin saturations for 7122 individuals. The transferrin saturations had been adjusted for diurnal variation and for the possible presence of an hereditary hemochromatosis allele in African Americans. Figure 1 shows the effect of diurnal variation on measurement of transferrin saturation for African American men. The graphs indicate the relationship between decile means for transferrin saturations, determined from blood drawn in morning examination sessions, and the corresponding mean transferrin saturations determined from blood drawn in afternoon sessions (Pearson correlation coefficient, r = 0.999) and evening sessions (r = 0.997). The regression equations based on the unweighted data (Figure 1A) are as follows:
Morning session transferrin saturation = 0.6 + 1.0 × (Afternoon session transferrin saturation). Morning session transferrin saturation = 1.5 + 1.2 × (Evening session transferrin saturation). These regression lines differed significantly as judged by the F test for the equality of regression lines (F = 262.2 with numerator and denominator degrees of freedom of 2 and 16, respectively; P < .0001). Similar results were found for data from African American women and Caucasian men and women. For each group, the correlation between decile means for transferrin saturations, determined from blood drawn in morning examination sessions, and the corresponding mean transferrin saturations determined from blood drawn in afternoon sessions and evening sessions was strong (r > 0.99 for all) and the regression lines differed significantly as judged by the F test for the equality of regression lines (F test, P < .0001 for all). Results of statistical mixture modeling indicated that the fit of the
unweighted data to a mixture of 3 normal populations with unequal
variances was significantly better than the fit to a mixture of 2 normal populations for African American men (likelihood ratio
statistic, 24.1, P < .01) and women (18.8, P < .01) as well as for Caucasian men (35.3, P < .01) and women (39.7, P < .01). Similarly, the fit of the data to a mixture of 3 normal populations was
significantly better than the fit to a single normal population for
all: African American men, likelihood ratio statistic, 221.1, P < .01, and women, 119.7, P < .01;
Caucasian men, 416.9, P < .01, and women, 469.4, P < .01. Figure 2 shows the distribution of
transferrin saturation for African American men (A) and women (B) and
for Caucasian men (C) and women (D). The fitted subpopulations are
superimposed over the histograms of the observed data. Table 2 gives mixture model parameter estimates
for transferrin saturation subpopulations and shows that for each
ethnic subgroup and gender, transferrin saturation subpopulations were
identified with increasing means (trend test P < .0001
for all). The findings are consistent with population genetics for
single major loci affecting the distribution of transferrin
saturations. For example, for African American men the sum of the
square roots of the proportion with the lowest transferrin saturation
(p = 0.854) and of the proportion with the highest
transferrin saturation (q = 0.146) was approximately 1 (p+q = 1.000). Corresponding results were found for
African American women (p = 0.905, q = 0.095)
and for Caucasian men (p = 0.902, q = 0.098)
and women (p = 0.927, q = 0.073) where
p+q = 1.000 for all.
Analysis of unweighted serum ferritin concentration data Our analysis of transferrin saturations demonstrated that 3 subpopulations of individuals could be detected within each ethnic subgroup and gender. We then compared the age-standardized mean serum ferritin concentrations for these subpopulations (Table 2). Trend tests demonstrated an increase in mean serum ferritin concentration, standardized to age 60, with increasing mean transferrin saturation for African American men (trend test 2 statistic with 1 degree of freedom, 1385, P < .0001) and women (858, P < .0001) and for Caucasian men (1746, P < .0001) and women (418, P < .0001). The
mean transferrin saturations within the middle and upper subpopulations
for African American men and women are significantly lower than those
for Caucasians (Table 2). For the second subpopulation, the mean
transferrin saturation value for African American men was 8% lower in
absolute value than that of Caucasian men (95% confidence interval
[CI] 6.8%, 9.1%). The mean transferrin saturation for African
American women was 10% lower in absolute value than that of Caucasian
women (8.7%, 11.6%). Considering the upper subpopulation, the mean
transferrin saturation values were 14% (8.2%, 19.2%) lower in
absolute value for African American men than in Caucasian men and 16%
(8.4%, 22.9%) lower in African American women compared to
Caucasian women.
Weighted results for African American and Caucasian populations Figure 1B confirms the effect of diurnal variation on measurement of transferrin saturation values using the weighted data. For African American men, the regression lines differed significantly (F = 192.9 with numerator and denominator degrees of freedom of 2 and 16, respectively; P < .0001). Table 3 gives the mixture modeling results after the data (adjusted for diurnal variation and age effects in African American and Caucasian individuals and for the potential presence of hemochromatosis in African Americans) were weighted to reflect the US population as a whole. The weighted results for transferrin saturation are similar to our primary results using unweighted data, which suggests that the unequal probability of selection in NHANES III did not have a major input on the transferrin saturation and serum ferritin concentration distributions. For example, for African Americans, the findings for weighted data are consistent with population genetics for a single major locus affecting the distribution of transferrin saturations. For men, the sum of the square roots of the proportion with the lowest transferrin saturation (p = 0.854) and of the proportion with the highest saturation (q = 0.146) is approximately 1 (1.000). Similarly, for women the sum of the square roots of the proportion with the lowest transferrin saturation (p = 0.904) and of the proportion with the highest saturation (q = 0.104) is also approximately 1 (1.0085). For both ethnic subgroups and each gender, trend tests demonstrated an increase in mean serum ferritin concentration, standardized to age 60, with increasing mean transferrin saturation (Table 3; P < .002 for all).
An estimated proportion of 0.861 of Caucasian men studied were included in a subpopulation with a mean transferrin saturation of 29.6%, whereas 0.134 comprised a subpopulation with an intermediate mean saturation of 48.3% and 0.005 formed a subpopulation with a mean saturation of 82.0%. An estimated proportion of 0.865 of Caucasian women studied were included in a subpopulation with a mean saturation of 25.9%, whereas 0.130 comprised a subpopulation with an intermediate mean saturation of 45.5% and 0.005 formed a subpopulation with a mean saturation of 79.3%. From these results, the estimated gene frequencies for hereditary hemochromatosis were 0.072 (0.070-0.075) for Caucasian men and 0.070 (0.060-0.081) for Caucasian women corresponding to prevalences of 5.2/1000 and 4.9/1000, respectively.
In the present study, we identified 3 transferrin saturation subpopulations in African Americans and Caucasians by using specialized modeling of data from NHANES III. The proportions of these subpopulations are consistent with Hardy-Weinberg criteria for a major, common genetic influence on iron metabolism in each of these 2 broad ethnic groups. We previously analyzed transferrin saturation data for African Americans in the NHANES II study and obtained similar findings regarding a possible major locus affecting iron metabolism.1 In this regard, it is of interest that an iron-loading genetic defect, distinct from the Cys282Tyr mutation in HFE that is responsible for most hemochromatosis in Caucasians,40 may be present in the sub-Saharan African population,41,42 and that mutations in HFE are very rare in the African American population.41 The present results are also consistent with the possibility of an iron-loading genetic defect in the African American population distinct from that in the Caucasian population and producing different phenotypes. For example, the mean transferrin saturations within the middle and upper subpopulations for African American men and women were from 8% (95% CI, 6.8%-9.1%) to 16% (8.4%-22.9%) lower, whereas mean serum ferritin concentrations were substantially higher than those for Caucasians of the same gender. In the present study, the estimated gene frequencies for hereditary hemochromatosis in Caucasians based on transferrin saturation subpopulations were 7.2% (7.0%-7.5%) for men and 7.0% (6.0%-8.1%) for women corresponding to prevalence estimates of 5.2/1000 for men and 4.9/1000 for women. These results are consistent with the reported prevalence of hereditary hemochromatosis and the frequency of the Csy282Tyr mutation in the HFE gene. For example, a meta-analysis by Lucotte and Mercier, comprising the results of recent studies conducted in 40 European populations, showed an overall frequency of 5.5% for the Cys282Tyr allele, corresponding to a prevalence estimate of 3/1000 (0.303%) for Cys282Tyr homozygotes.43 Since NHANES III was conducted before the discovery of the HFE gene, a limitation of this study is that testing for the Cys282Tyr mutation and other mutations was not performed. About 28% of Caucasians have the His63Asp mutation in the HFE gene.22,44,45 Heterozygotes for His63Asp have only minor changes in the transferrin saturation22 and are probably included in the lower Caucasian subpopulation of the analysis of the present study, whereas homozygotes for His63Asp and compound heterozygotes (Cys282Tyr/His63Asp) have elevations in transferrin saturation similar to Cys282Tyr heterozygotes22,46 and are probably included in the middle Caucasian subpopulation. Only about 10% of African Americans have the His63Asp mutation with most being heterozygotes and less than 0.5% being homozygotes.22 In the present analysis of transferrin saturations from African Americans, it is possible that His63Asp heterozygotes were included in the lower subpopulation, whereas His63Asp homozygotes were included in the middle subpopulation. Among Caucasian women, our finding of an elevation in mean serum ferritin for the middle subpopulation is consistent with the results found for Cys282Tyr heterozygotes in the large study conducted by Beutler and colleagues,22 but in contrast to other investigations in which Caucasian women, heterozygous for the Cys282Tyr mutation, did not have elevations in ferritin as compared to normal homozygotes.44,46 We have considered the possibility that adjustment of transferrin saturations for diurnal variation potentially could introduce an element of artifact in the analyses. To evaluate this possibility, we reanalyzed the data using the unadjusted values. As in the analyses of the adjusted data, these reanalyses identified 3 transferrin saturation subpopulations with increasing means for each ethnic subgroup and gender (trend test P < .0001 for all), although the estimated percentage of cells in corresponding subpopulations differed slightly (results not shown). Similarly, before correction for diurnal variation, the means for transferrin saturation within the middle and upper subpopulations for African American men and women were 5% (4.4%-6.4%) to 16% (7.3%-24.1%) lower, and the means for serum ferritin concentration were substantially higher than those for Caucasians of the same gender. Trend tests also demonstrated an increase in mean serum ferritin concentration, standardized to age 60, with increasing mean transferrin saturation for each group (P < .0001 for all), consistent with the results observed in the analyses of the adjusted data. Even among Cys282Tyr homozygotes there is considerable variability in disease expression,22,47 and the place of testing for the Cys282Tyr mutation in screening for hemochromatosis has not been determined. Although serum ferritin concentration was once suggested as a screening marker for homozygosity for the hemochromatosis allele,48 transferrin saturation has been regarded for some time as the best single screening test for hemochromatosis.49,50 Recent large studies of Caucasians in which both phenotypic testing with transferrin saturation and genotypic testing for the Cys282Tyr mutation were performed have shown that screening with transferrin saturation does not identify all Cys282Tyr homozygotes,22,47 underscoring the fact that the ideal approach to phenotypic screening for hemochromatosis has not yet been developed. In our study of NHANES III data using statistical modeling methodology, subpopulations identified on the basis of increasing transferrin saturation had progressively increasing mean age-adjusted serum ferritin levels in each ethnic group, stratified by gender. These results suggest that it may be possible to develop a practical approach to the initial screening for iron overload that includes the combination of transferrin saturation and serum ferritin concentration measurements.
We thank Lamman Doan for statistical computing support. We gratefully acknowledge the contributions of Jacqueline Lovingood, CDC analyst who performed specimen analyses for serum iron and total iron-binding capacity in the NHANES Laboratory, and Della Twite, CDC analyst who performed analyses for serum ferritin.
Submitted February 2, 2001; accepted June 15, 2001.
Supported in part by grants and contracts from the National Institutes of Health, HL-508203 (to C.E.M.), N01-HC-05190 (to C.E.M. and G.D.M.), UH1 HL03679-03 (to V.R.G.), and N01-HC-05186 (to V.R.G). Additional support was provided by the Department of Veterans Affairs (to G.D.M. and C.E.M.) and the National Center for Health Statistics (to C.E.M. and V.H.).
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 U.S.C. section 1734.
Reprints: Christine E. McLaren, University of California, Irvine, Epidemiology Division, Dept of Medicine, 224 Irvine Hall, Irvine, CA 92697-7550; e-mail: cmclaren{at}uci.edu.
1.
Gordeuk VR, McLaren CE, Looker AC, Hasselblad V, Brittenham GM.
Distribution of transferrin saturations in the African-American population.
Blood.
1998;91:2175-2179
2.
McLaren CE, Gordeuk VR, Looker AC, et al.
Prevalence of heterozygotes for hemochromatosis in the white population of the United States.
Blood.
1995;86:2021-2027 3. Jandl JH. Blood. Boston: Little, Brown; 1987. 4. Edwards CQ, Griffen LM, Goldgar D, et al. Prevalence of hemochromatosis among 11,065 presumably healthy blood donors. N Engl J Med. 1988;318:1355-1362[Abstract].
5.
Looker AC, Johnson CL.
Prevalence of elevated serum transferrin saturation in adults in the United States.
Ann Intern Med.
1998;129:940-945 6. Plan and operation of the Third National Health and Nutrition Examination Survey, 1988-94. DHHS publication no. (PHS) 94-1308, (Vital and Health Statistics; series 1; no. 32). Hyattsville, MD: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Health Statistics; 1994. 7. Gunter EW, Lewis BL, Koncikowski SM. Laboratory procedures used for the third National Health and Nutrition Examination Survey (NHANES III), 1988-1994 [CD-ROM]. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, Center for Environmental Health, and Hyattsville, MD: National Center for Health Statistics; 1996. Available from National Technical Information Service, Springfield, VA. 8. Finch CA, Deubelbeiss K, Cook JD, et al. Ferrokinetics in man. Medicine. 1970;49:17-53[Medline] [Order article via Infotrieve]. 9. Hershko C, Graham G, Bates GW, Rachmilewitz EA. Non-specific serum iron in thalassaemia: an abnormal serum iron fraction of potential toxicity. Br J Haematol. 1978;40:255-263[Medline] [Order article via Infotrieve]. 10. Cartwright GE, Wintrobe MM. Chemical, clinical, and immunological studies on the products of human plasma fractionation; XXXIX, the anemia of infection. Studies on the iron-binding capacity of serum. J Clin Invest. 1949;28:86-98. 11. Bainton DF, Finch CA. The diagnosis of iron deficiency anemia. Am J Med. 1964;37:62-70[CrossRef][Medline] [Order article via Infotrieve]. 12. Edwards CQ, Griffen LM, Kaplan J, Kushner JP. Twenty-four hour variation of transferrin saturation in treated and untreated hemochromatosis homozygotes. J Intern Med. 1989;226:173-179. 13. Gordeuk VR, Brittenham GM, McLaren GD, Spagnuolo PJ. Hyperferremia in immmunosuppressed patients with acute nonlymphocytic leukemia and the risk of infection. J Lab Clin Med. 1986;108:466-472[Medline] [Order article via Infotrieve]. 14. Barton JC, Bertoli LF, Rothenberg BE. Peripheral blood erythrocyte parameters in hemochromatosis: evidence for increased erythrocyte hemoglobin content. J Lab Clin Med 2000;135:96-104[CrossRef][Medline] [Order article via Infotrieve].
15.
Cartwright GE, Hugeley CM, Ashenbrucker H, et al.
Studies on free erythrocyte protoporphyrin, plasma iron and plasma copper in normal and anemic subjects.
Blood.
1948;3:501-525 16. Devasthali SD, Gordeuk VR, Brittenham GM, et al. Bioavailability of carbonyl iron: a randomized, double-blind study. Eur J Haematol. 1991;46:272-278[Medline] [Order article via Infotrieve]. 17. Bell H, Skinningsrud A, Raknerud N, Try K. Serum ferritin and transferrin saturation in patients with chronic alcoholic and non-alcoholic liver diseases. J Intern Med. 1994;236:315-322[Medline] [Order article via Infotrieve]. 18. Brendstrup P. Serum copper, serum iron and total iron binding capacity of serum in patients with chronic rheumatoid arthritis. Acta Med Scand. 1953;146:384-392. 19. Cartwright GE. The anemia of chronic disorders. Semin Haematol. 1966;3:351-375[Medline] [Order article via Infotrieve]. 20. Hamilton LD, Gubler CJ, Cartwright GE, Wintrobe MM. Diurnal variation in the plasma iron level of man. Proc Soc Exp Biol Med. 1950;75:65-68. 21. Chakraborty R, Kamboh MI, Nwankwo M, Ferrell RE. Caucasian genes in American blacks. New data. Am J Hum Genet. 1992;50:145-155[Medline] [Order article via Infotrieve].
22.
Beutler E, Felitti V, Gelbart T, Ho N.
The effect of HFE genotypes on measurements of iron overload in patients attending a health appraisal clinic.
Ann Intern Med
2000;133:329-337 23. Barton JC, Acton RT. Transferrin saturation phemotype and HFE genotype screening for hemochromatosis and primary iron overload: predictions from a model based on national, racial, and ethnic group composition in central Alabama. Genet Test. 2000;4:199-206[CrossRef][Medline] [Order article via Infotrieve]. 24. McLaren CE, McLachlan GJ, Halliday JW, et al. Distribution of transferrin saturation in an Australian population: relevance to the early diagnosis of hemochromatosis. Gastroenterology. 1998;114:543-549[CrossRef][Medline] [Order article via Infotrieve]. 25. Hasselblad V. Manual for the grouped DISFIT software. Version 1.1. Durham, NC: Duke University; 1992. 26. McLaren CE, Brittenham GM, Hasselblad V. Analysis of the volume of red blood cells: application of the expectation-maximization algorithm to grouped data from the doubly-truncated lognormal distribution. Biometrics. 1986;42:143-158[CrossRef][Medline] [Order article via Infotrieve].
27.
McLaren CE, Brittenham GM, Hasselblad V.
Statistical and graphical evaluation of erythrocyte volume distributions.
Am J Physiol.
1987;252:H857-H866 28. McLachlan GJ, Basford KE. Mixture models: inference and applications to clustering. New York, NY: Marcel Dekker; 1988. 29. Crump KS, Howe R. A preview of methods for calculating confidence limits in low dose extrapolation. In: Clayton DB,Krewski D,Munro IC, eds. Toxicological risk assessment. Boca Raton, FL: CRC Press; 1985.
30.
Expert Scientific Working Group.
Summary of a report on assessment of the iron nutritional status of the United States population.
Am J Clin Nutr.
1985;42:1318-1330 31. Milman N. Serum ferritin in Danes: studies of iron status from infancy to old age, during blood donation and pregnancy. Int J Hematol. 1996;63:103-135[CrossRef][Medline] [Order article via Infotrieve]. 32. McCord JM. Iron, free radicals, and oxidative injury. Semin Hematol. 1998;35:5-12[Medline] [Order article via Infotrieve].
33.
Cook JD, Finch CA, Smith NJ.
Evaluation of the iron status of a population.
Blood.
1976;48:449-455
34.
Leggett BA, Brown NN, Bryant SJ, et al.
Factors affecting the concentrations of ferritin in serum in a healthy Australian population.
Clin Chem.
1990;36:1350-1355 35. Custer EM, Finch CA, Sobel RE, et al. Population norms for serum ferritin. J Lab Clin Med. 1995;126:88-94[Medline] [Order article via Infotrieve]. 36. Pilch SM, Senti FR. Assessment of the iron nutritional status of the US population based on data collected in the Second National Health and Nutrition Examination Survey, 1976-80. Bethesda, MD: Life Sciences Research Office, Federation of American Societies for Experimental Biology; August 1984. 37. Zacharski LR, Ornstein DL, Woloshin S, Schwartz LM. Association of age, sex, and race with body iron stores in adults: analysis of NHANES III data. Am Heart J. 2000;140:98-104[CrossRef][Medline] [Order article via Infotrieve]. 38. Cuzick Jack. Trend tests. In: Kotz S,Johnson N, eds. Encyclopedia of Statistical Sciences. Vol 9. New York, NY: John Wiley; 1988:336-342. 39. Mohadjer L, Montaquila J, Waksberg J, et al. National Health and Nutrition Examination Survey III. Weighting and Estimation Methodology. Rockville, MD: WESTAT; 1996. 40. Feder JN, Gnirke A, Thomas W, et al. A novel MHC class I-like gene is mutated n patients with hereditary haemochromatosis. Nat Genet. 1996;13:399-408[CrossRef][Medline] [Order article via Infotrieve]. 41. Gordeuk VR, Mukiibi J, Hasstedt SJ, et al. Iron overload in Africa: interaction between and gene and dietary iron content. N Engl J Med. 1992;326:95-100[Abstract]. 42. McNamara L, MacPhail AP, Gordeuk VR, Hasstedt SJ, Rouault T. Is there a link between African iron overload and the described mutations of the hereditary haemochromatosis gene? Br J Haematol. 1998;102:1176-1178[CrossRef][Medline] [Order article via Infotrieve]. 43. Lucotte G, Mercier G. Celtic origin of the C282Y mutation of hemochromatosis. Genet Test. 2000;4:163-169[CrossRef][Medline] [Order article via Infotrieve].
44.
Burt MJ, George PM, Upton JD, et al.
The significance of haemochromatosis gene mutations in the general population: implications for screening.
Gut.
1998;43:830-836 45. Guttridge MG, Carter K, Worwood M, Darke C. Population screening for hemochromatosis by PCR using sequence-specific primers. Genet Test. 2000;4:111-114[CrossRef][Medline] [Order article via Infotrieve].
46.
Rossi E, Olynyk JK, Cullen DJ, et al.
Compound heterozygous hemochromatosis genotype predicts increased iron and erythrocyte indices in women.
Clin Chem.
2000;46:162-166 47. Adams PC, Kertesz AE, McLaren CE, Barr R, Bamford A, Chakrabarti S. Population screening for hemochromatosis: a comparison of unbound iron-binding capacity, transferrin saturation, and C282Y genotyping in 5,211 voluntary blood donors. Hepatology. 2000;31:1160-1164[CrossRef][Medline] [Order article via Infotrieve]. 48. Beaumont C, Simon M, Fauchet R, et al. Serum ferritin as a possible marker of the hemochromatosis allele. N Engl J Med. 1979;301:169-174[Abstract]. 49. Dadone MM, Kushner JP, Edwards CQ, et al. Hereditary hemochromatosis: analysis of laboratory expression of the disease by genotype in 18 pedigrees. Am J Clin Pathol. 1982;78:196-207[Medline] |
Top
Abstract
Introduction
-glutamyl transferase, serum total bilirubin, and serum lactate dehydrogenase) or who had a positive or borderline serum hepatitis B surface antigen or positive serum hepatitis C antibody because transferrin saturation and serum ferritin concentration elevations can be associated with hepatocellular damage attributable to
concurrent viral disease and alcohol or drug usage.
