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Division of Nutrition and Physical Activity, Centers for Disease Control and Prevention, Atlanta, GA;
* Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA;
Department of Nutrition, The Pennsylvania State University, State College, PA; and
** Division of Nutritional Sciences, Cornell University, Ithaca, NY
1To whom correspondence should be addressed. E-mail: zmei{at}cdc.gov.
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ABSTRACT |
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 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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0.2 SDU in all trials and was significant in 7. Hb changed by 0.2 SDU in 6 and was significant in 5. TfR increased by 0.2 SDU in 5 of 8 interventions in which it was measured and was significant in 4. ZPP increased by 0.2 SDU and was significant in 3 of 6 interventions. Excluding Hb, the indicator with the largest change in SDU was ferritin in 4 trials, TS in 2 trials, body-iron store in 2 trials, and TfR in 1. In the 2 cases in which body-iron stores showed the largest change, the change in ferritin was nearly as large. Our results suggest that with currently available technologies, ferritin shows larger and more consistent response to iron interventions than ZPP or TfR. We cannot make confident inference about MCV or TS, which were included in only 4 and 2 trials, respectively. It is possible that the optimal indicator(s) may differ with age, sex, and pregnancy. There were too few trials in each age and sex group to allow us to explore this question.
KEY WORDS: • iron deficiency • hemoglobin • ferritin • transferrin receptor
Iron deficiency is generally recognized as the most common nutritional deficiency worldwide (1–3) and the effectiveness of interventions such as dietary modification and food fortification is often questioned. One reason for the apparent lack of success may be the use of indicators that lack the sensitivity and specificity necessary to characterize changes in iron status related to effective iron interventions. Hematological and specific biochemical indicators related to iron storage or erythropoesis are usually used alone or in various combinations, but there is no consensus about the best indicator or indicators to monitor the response of populations to iron interventions, particularly in areas with limited resources.
Hemoglobin (Hb)2 is most commonly used because it is inexpensive, easy to perform, and rapid. However, Hb levels are affected by factors other than iron deficiency because Hb is a function of RBC production and turnover. Hb levels therefore lack specificity for categorizing iron status. Moreover, mild iron deficiency may not affect Hb levels (4–6). Indicators of iron deficiency are both more specific and more sensitive, but they are more expensive and involve more complicated assay techniques (7–12). In addition, the biochemical tests for iron deficiency each measure a different aspect of iron physiology (4–12). Indicators are used for different purposes in clinical screening and diagnosis vs. public health assessment at a population level, and different indicators may perform best for different tasks (13). For the purpose of measuring the population response to an iron intervention, the best indicator of iron deficiency would be the one that shows the largest and most consistent change in response to an increase in bioavailable iron intake.
The WHO and the U.S. CDC held a joint technical consultation on the "Assessment of Iron Status at the Population Level" in Geneva, Switzerland on April 6–8, 2004 to review laboratory indicators currently available to assess iron status in population studies, to select the best indicator(s) with which to assess the iron status of populations, and to select the most efficient indicator(s) to evaluate the impact of interventions to control iron deficiency in populations. Efficiency in this context means the indicator(s) that can detect a true change in iron status of a population using the fewest and simplest tests. To provide empirical, population-based evidence for this consultation, we analyzed the data from 9 iron intervention trials. The objective of this analysis was to compare the magnitude and consistency of the response of different indicators of iron status, at the population level, in effective iron supplementation or fortification trials.
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SUBJECTS AND METHODS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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For each of the 9 studies, 4–5 mL venous blood was drawn into EDTA-coated tubes and handled by trained specialists for further tests. Hb and zinc protoporphyrin (ZPP) were measured in whole blood using the cyanomethemoglobin methods by an electronic counter or by HemoCue system for Hb and using a hematofluorometer for ZPP. Transferrin receptor (TfR) was measured by ELISA. Ferritin (SF) was measured using fluorescent-linked immunoassays (19) or ELISA. Mean cell volume (MCV) was measured by an electronic counter. Serum iron and total iron-binding capacity (TIBC) were measured by standard colorimetric methods, and transferrin saturation (TS) was then calculated from these 2 measurements. All of the study samples that had TfR measurement were assayed in duplicate or triplicate and at least 8 of the 9 study samples for all of the other indicators were measured in duplicate or triplicate also. All of the samples had internal as well as external standards for quality control. The details of the data collection and laboratory procedures were published elsewhere (14–20).
Before the WHO/CDC consultation, a WHO/CDC working group met in January 2004 to review the literature on indicators of iron status and to select the indicators considered to be the best for discussion by the consultation. The 5 indicators, Hb, SF, MCV, ZPP or erythrocyte protoporphyrin, and TfR, were chosen for their theoretical advantage and practicality of measurement. All 9 studies included measurement of Hb and SF, and all but 1 included TfR. Measurement of ZPP or MCV was included in several of the studies, but not all. We also examined TS in the 2 studies in which it was measured.
We examined the performance of each of these indicators in measuring a change due to the iron intervention. We also examined the performance of several transformations of these indicators. Both SF and TfR have distributions that are highly skewed to the right. Thus, we examined logarithm-transformed SF [ln(SF)] or TfR [ln(TfR)]. In cases in which SF was recorded as 0.0, we used ln(0.1). Total body-iron stores were calculated using SF and TfR in an equation proposed by Cook (21): Body-iron store (mg/kg) = –[log10(TfR · 1000/SF) – 2.8229]/0.1207.
To avoid the influence of distant outliers on our results, we deleted observations with extreme values of the iron indicators. We considered values > 4 SD above or below the median to be implausible and thus deleted these observations. Because the calculation of SD is itself strongly influenced by outliers, however, we used an alternative algorithm to estimate the SD, based on the median, 5th, and 95th percentile values of each distribution. Low-end outlier cutoff values were calculated as follows: median – [(median – 5th percentile value)/1.645] · 4, and high-end outlier cutoff values were calculated as median + [(95th percentile value – median)/1.645] · 4.
Because we intended to compare the performance of various iron indicators, we excluded subjects with any outlier or missing values, from either baseline or follow-up, for any of the indicators measured in each study. Thus the sample size across indicators was identical in each study. Details on the number of records excluded due to missing or outlier values appear in Appendix 1 and the final sample size for this analysis is summarized in Table 1.
The key outcome of interest was the magnitude of change in each indicator for the intervention group compared with that for the control group. For example, in the intervention group of the Moroccan schoolchildren study, mean SF rose from 21.55 µg/L at baseline to 30.92 µg/L at final follow-up. By comparison, mean SF in the control group rose from 21.75 to 24.35 µg/L. In this case, our estimated magnitude of change resulting from the intervention is [(30.92 – 21.55) – (24.35 – 21.75)] = 6.77 µg/L. However, because each indicator uses a different unit of measurement, comparing changes among indicators is difficult. We therefore standardized each indicator’s magnitude of change by expressing it in SD units (SDU) to ensure that the response to the iron interventions would be comparable across all indicators within a study.
We first calculated each indicator’s SD at baseline, combining the control- and intervention-group observations for each study, then divided the magnitude of change by the SD, such that the change for all indicators was now expressed in SDU. For example, in the Moroccan case described above, the SD of serum ferritin at baseline was 15.38 µg/L, yielding a magnitude of change for serum ferritin of 0.44 SDU (6.77/15.38).
The use of the SDU in this analysis is similar to examining the change in Z-score between an intervention and control group as could be applied to anthropometric data (22); however, the computation of the SDU differs from that of the Z-score. The Z-score compares the actual value of an individual at 1 point in time to a reference mean and SD for that age and sex group. If we examined the change in Z-scores in the intervention compared with the control groups, the reference means would be irrelevant; thus our calculation is similar. However, there is no reference population for the SD in iron status measures; hence we used the SD of the combined intervention and control groups for the specified study and indicator at baseline. To avoid confusion with Z-score, we prefer the term SDU in this analysis.
To summarize results across the studies, we looked at 3 summary statistics for each iron indicator: the number of studies that showed a significant magnitude of change for the indicator; the number of studies that showed a magnitude of change of at least 0.2 SDU for the indicator; and the number of studies in which the indicator showed the largest change. Although 0.2 SDU is arbitrary, power calculations indicated that a change of 0.2 SDU could be detected with a sample size of 400 subjects per study group based on a normal distribution comparing 2 independent samples with equal variances (
= 0.05, power = 0.8).
For the analysis of the largest change, we did not count Hb as one of the indicators because we expected Hb to be measured regularly; in fact, we were looking for a second, more specific biochemical indicator of iron deficiency. For each of the summary statistics, we counted only the studies in which the indicator changed in the expected direction. For example, ln(SF), MCV, TS, and total body-iron stores should increase, and ln(TfR) and ZPP should decrease.
We also examined changes in the 10th percentile for indicators expected to increase [e.g., ln(SF)], and the 90th percentile for those expected to decrease (e.g., ZPP). If the iron intervention had a greater effect on persons who were initially more iron deficient, we might expect the intervention to cause a greater shift in the respective percentile than in the mean. Shifts in percentiles were expressed in SD units and summarized in the same way as was done for the mean.
Finally, we examined the absolute changes in prevalence of anemia or iron deficiency, using the WHO cutoff values (7) for Hb, SF, and ZPP, and CDC suggested cutoff values (4) for MCV and TS. High TfR was defined as 8.0 mg/L, and low body-iron store was defined as <0 mg/kg. Absolute changes in prevalence were calculated by subtracting the change in prevalence for the control group from that for the intervention group. We considered a net change in prevalence of 10% to be noteworthy and thus counted the studies with at least this magnitude of change.
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RESULTS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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0.2 SDU in 5 of the 8 studies in which it was measured, and the change was significant in 4 studies. Hb changes exceeded 0.2 SDU in 6 studies and changed significantly in 5 studies. MCV, ZPP, and TS were measured in only a few studies and therefore had few opportunities to show large effects. TS showed the largest change in the 2 studies in which it was measured. Body-iron stores showed the largest change in 2 of the 8 studies in which they were measured.
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DISCUSSION |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Several studies showed significant differences in baseline measures of iron status between the control and intervention groups (Table 2), despite randomization. Our methodology largely accounted for these differences by calculating summary "magnitude of change" statistics; however, regression to the mean effects would tend to generate larger baseline-to-follow-up changes in subgroups with higher rates of iron deficiency, thus affecting our results. To account for the significant difference between control and intervention group at baseline on some indicators, we also used a linear regression model to adjust for the different measurement values at baseline for each indicator. The results of this additional analysis were not substantively different from those presented here (unpublished data).
We also examined shifts in the median rather than the mean, to assess whether any of the results might have been determined by outlier values or skewed distributions affecting the mean calculation. Results were not substantially different from those presented in Table 3 (unpublished data). We also repeated the analysis of Table 3 including all of the outliers we defined in Appendix 1 to determine whether those outliers could change our conclusion. Again, results were not substantially different from those presented in Table 3 (unpublished data).
In addition, we examined the mean of untransformed SF and TfR, and the results did not differ substantially from those for ln(SF) and ln(TfR). Alternative ratios of TfR and SF [TfR:SF and TfR:ln(SF)] were also examined to test whether these different computations might yield better results. However, the performances of these ratios were not better than those of the body-iron stores (unpublished data).
The strengths of this study lie in the selection of data from double-blind, randomized, placebo-controlled, clinical trials. We can be relatively confident that the changes in iron indicators are in fact due to iron intervention because other factors that might affect both the hematological and biochemical indicators would be expected to be distributed randomly between the intervention and control groups. Subtraction of control changes from intervention changes would then eliminate any effect of these confounding factors. Moreover, the interventions were very likely to have had a substantial effect on iron status because a bioavailable form of iron in an adequate dose was used in a trial of sufficient duration. Finally standardization of the changes by using SDUs permitted direct comparisons of effect.
The study also had limitations. First, the 9 studies included here were very diverse and included children aged between 4 mo and 15 y, nonpregnant women of childbearing age, and pregnant women. It is possible that the optimal indicator(s) may differ with age, sex, and pregnancy. There were too few trials in each age and sex group to allow us to explore this question; thus, the response of iron indicators should be investigated in future iron intervention trials among diverse populations to test our results.
Second, the iron dosages and durations of the interventions varied across trials. We considered all of the trials to have had a very high chance of successfully improving iron status, and it is important to note that our analysis examined the performance of the indicators only within each study and did not compare interventions or studies with each other.
Finally TS was measured in only 2 of the studies and MCV in 4, making it difficult to draw any conclusions about these 2 indicators. From a practical point of view the absence of these 2 indicators from the analysis may be relatively unimportant because we were attempting to define the most practical approach to evaluating iron interventions in developing countries. The use of TS is unlikely to be feasible for such field trials because of the technical complexity of obtaining reliable results in the field setting in a developing country. Accurate measurements of MCV necessitate the employment of particle counters that are unlikely to be available and adequately maintained and standardized outside of well-established hematology laboratories.
Infection/inflammation will affect some of the measurements we evaluated in this analysis, particular SF because it is an acute phase reactant. Only 3 of the 9 studies assessed had a marker of infection/inflammation. We had access to these data for only 1 study and thus were unable to perform a subanalysis. In our analysis, randomization should help ensure comparability between control and intervention groups because infection and inflammation would affect the iron indicators in both groups to a similar extent. Without a comparable control group, infection/inflammation can bias the response of indicators to iron interventions if the rates of infection/inflammation vary from baseline to follow-up.
It is important to note that our analysis was designed to evaluate the response to iron supplementation or fortification. Ferritin is a measure of iron sufficiency and gives no information about the magnitude of the iron deficit in individuals with absent iron stores. It may therefore be less useful in studies designed to define the extent of iron deficiency in surveys. Theoretically, the combination of SF and TfR (e.g., by calculating the distribution of body-iron stores) would provide additional information concerning the magnitude of iron deficiency. However, in this analysis, body-iron stores in general performed similarly to SF. Given the additional cost of an extra test for TfR, we see little justification to advocate its use for evaluating the response to an iron intervention at the present time. Its use in combination with SF should be reconsidered once an international reference standard for TfR is available.
APPENDIX 1 Data exclusion1, 2
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1 C, control group; I, intervention group.
2 Subjects with missing indicator values at baseline or follow-up visits were excluded.
3 The 2 intervention groups with different iron compounds but similar doses were combined.
4 Group 3 of this study (treated with iron intervention for 6–9 mo, yielding 76 observations) was excluded from this analysis.
APPENDIX 2 Prevalence of anemia defined by low Hb and iron deficiency defined by low SF, high serum TfR, high ZPP, low MCV, low TS, or low total body-iron store1
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1 C, control group; I, intervention group; B, baseline; F, follow-up.
2 Low Hb cutoff points: <11.0 g/dL for preschool children and pregnant women; <12.0 g/dL for school-aged children and nonpregnant women.
3 Low SF cutoff points: <12.0 µg/L for preschool children; <15.0 µg/L for school-aged children, pregnant and nonpregnant women.
4 High TfR cutoff point: >8.0 mg/L.
5 High ZPP cutoff points: >61.0 mmol/mol heme for preschool children; >70.0 mmol/mol heme for school-aged children, pregnant and nonpregnant women.
6 Low MCV cutoff points: <74.0 fL for preschool children; <81.0 fL for school-aged children, pregnant and nonpregnant women.
7 Low TS cutoff point: <15.0%.
8 Low body-iron store cutoff point: <0 mg/kg.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Manuscript received 24 January 2005. Initial review completed 27 February 2005. Revision accepted 12 May 2005.
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LITERATURE CITED |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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19. Stoltzfus, R. J., Chway, H. M., Montresor, A., Tielsch, J. M., Jape, J. K., Albonico, M. & Savioli, L. (2004) Low dose daily iron supplementation improves iron status and appetite but not anemia, whereas quarterly anthelminthic treatment improves growth, appetite and anemia in Zanzibari preschool children. J. Nutr. 134:348-356.
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