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Original Articles: Gastroenterology: Inflammatory Bowel Disease

Use of Reticulocyte Hemoglobin Content in the Assessment of Iron Deficiency in Children With Inflammatory Bowel Disease

Syed, Sana∗,†; Kugathasan, Subra∗,†; Kumar, Archana; Prince, Jarod; Schoen, Bess T.∗,†; McCracken, Courtney; Ziegler, Thomas R.; Suchdev, Parminder S.∗,†

Author Information
Journal of Pediatric Gastroenterology and Nutrition: May 2017 - Volume 64 - Issue 5 - p 713-720
doi: 10.1097/MPG.0000000000001335


What Is Known

  • Iron deficiency and anemia affect 50% to 75% of patients with inflammatory bowel disease.
  • Iron deficiency is an important contributor to anemia but is difficult to diagnose given the effect of inflammation on iron biomarkers.
  • In the setting of inflammation, reticulocyte parameters (eg, the hemoglobin content of reticulocytes [CHr]) have been considered promising.
  • What Is New
  • We showed that CHr is affected by inflammation in a cross-sectional pediatric inflammatory bowel disease population.
  • Most clinically available iron biomarkers are affected by inflammation and should be adjusted; we suggest using a combination of iron status biomarkers given that there is no single iron status biomarker that predicts iron deficiency in pediatric inflammatory bowel disease.

Iron deficiency has significant negative morbidity with regards to quality of life and can lead to abnormal growth and inadequate cognitive development in children and adolescents (1). Prevalence estimates of iron deficiency and anemia in inflammatory bowel disease (IBD) have been reported as high as 50% to 75%, with approximately 50% being isolated iron deficiency with no anemia (2). Iron deficiency can be caused by an absolute iron deficiency (low iron stores, through a low dietary intake, malabsorption, and/or gastrointestinal bleeding) or a functional iron deficiency (inflammation-associated cytokinemia with upregulated hepcidin production and resulting iron sequestration in enterocytes and macrophages and restricted erythropoiesis, resulting in anemia of chronic disease (3–5)). Micronutrient deficiencies often coexist in the IBD population (1), including in particular iron and vitamin A deficiencies, which have a role in microcytic anemia (6). Iron deficiency causes anemia that is associated with significant morbidity, including increased susceptibility to infection, reduced functionality of the musculoskeletal system, impaired intellectual functioning, and subsequent impaired work capacity (1,7). Anemia of chronic disease is anemia due to iron sequestration in patients with autoimmune disorders (such IBD), cancer, infections, and chronic kidney diseases (4). If left untreated, iron deficiency is associated with decreased quality of life with abnormal growth and poor cognitive development in children and adolescents (8,5). Studies investigating the influence of vitamin A supplementation on iron status have shown simultaneous use of iron and vitamin A supplements seemed to be more effective to prevent iron-deficiency anemia than the use of these micronutrients alone (6). Low vitamin A status has been documented both in patients with Crohn disease (CD) and in patients with ulcerative colitis (UC); furthermore, a relation between disease severity and vitamin A deficiency has been observed among patients with CD (9).

Iron status can be assessed in several ways with stainable bone marrow as the criterion standard; however, most biomarkers are unreliable in the setting of inflammation as is often the case in patients with IBD, with or without acute disease exacerbations (7). The American Academy of Pediatrics and World Health Organization (WHO)/Centers of Disease Control and Prevention guidelines for iron deficiency assessment in otherwise healthy children recommend the measurement of ferritin and/or soluble transferrin receptor (sTfR) along with assessment of inflammation by C-reactive protein (CRP) and/or α1-acid glycoprotein (AGP) (10,11). IBD guidelines recommend measurement of ferritin or transferrin saturation along with CRP as a measure of inflammation (12). Ferritin is an indicator of the storage iron content, but is itself an acute phase reactant and is therefore an unreliable measure of iron stores if inflammation is present (13). The challenge with using ferritin as an iron status biomarker in IBD is that the prevalence of iron deficiency is underestimated, if the effect of inflammation is unaccounted for. Serum sTfR levels are increased in conditions of low iron availability for erythropoiesis, but in anemia of chronic disease are normal, due to the fact that transferrin receptor expression is negatively affected by inflammatory cytokines (14). Serum transferrin saturation is low in both iron deficiency and anemia of chronic disease, so is not helpful in distinguishing between the 2 (14). Hepcidin has been prospectively demonstrated to negatively correlate with hemoglobin (Hgb) levels, supporting the hypothesis that IL-6–driven hepcidin production mediates anemia of chronic disease in patients with CD (15). There remains a pressing clinical need for better methods to accurately determine iron status in IBD to guide therapy that may improve clinical outcomes (16).

The hemoglobin concentration of reticulocytes (CHr) (16) is a marker of iron status, which has been shown to be an early and sensitive index of erythropoiesis (17), given the short 1- to 2-day lifespan of the reticulocyte (18). CHr is considered to be an initial and reliable indicator of iron-deficiency anemia, and unlike many other iron markers, is not affected by inflammation (17,18). CHr is also an early indicator of the functional iron available for new red blood cell (RBC) production over the previous 3 to 4 days (17). In patients with IBD, there are no studies to date evaluating CHr as a reliable index to assess iron status or in response to iron supplementation therapy. We hypothesized that in pediatric patients with IBD, there is no association between CHr and markers of inflammation when compared to standard markers of iron status (ferritin and sTfR). Our aims were to determine the association of indices of iron status (CHr, ferritin, sTfR) with inflammation in pediatric IBD patients; describe the prevalence of iron deficiency, anemia, and iron-deficiency anemia in children with IBD; and assess the sensitivity and specificity of CHr as a measure of iron status in this cohort.


Study Population and Sample

We performed a single-center, cross-sectional study among children presenting to the pediatric IBD clinics, outpatient infusion clinics, emergency department, and the inpatient gastroenterology service at the Children's Healthcare of Atlanta and Emory University. Inclusion criteria were age of the participant of 5 to 18 years; confirmed diagnosis of IBD (CD, UC, or IBD-unclassified) by a pediatric gastroenterologist. Patients meeting the following exclusion criteria were not considered eligible for the study: patients with additional acute stress/inflammation such as those with history of any surgeries or infections within a 1-month period before study entry; history of inherited blood disorders (thalassemia, sickle anemia, or trait); history of receiving any packed RBC infusion within 120 days of study enrollment; pregnant adolescents; current mean corpuscular volume (MCV) of >100 fL/cell (because of a presumption of vitamin B12 or folic acid deficiency which can also lead to anemia); any iron supplementation. Patients were screened by review of all available prior clinical laboratory data within a 1-month time frame before their study visit to assess presence of inflammation and iron deficiency (Fig. 1). All patients in our study population had disease surveillance laboratory investigations (complete blood count, CRP) at routine intervals as part of their clinical care, which we used as part of our screening. This allowed us to stratify our enrollment to target the following spectrum of patients: subjects with/without inflammation (CRP >5 mg/L) and with/without iron deficiency (MCV <75 fL/cell or red blood cell distribution width [RDW] >14.5% or anemia). Enrollment was done for a 6-month period from May 2014 to November 2014.

Flow of study participants during 6-month enrollment period from May 2014 to November 2014 (n = 62).

Sample Collection and Laboratory Assays

Blood was collected from all subjects at the time of enrollment in a venous blood collection tube coated with ethylenediaminetetraacetic acid (Becton, Dickinson and Company, Franklin Lakes, NJ). Whole blood samples of 3 to 5 mL each were immediately sent for the following laboratory tests per standard clinical protocols: complete blood count (Siemens Advia 2120 and 120, Erlangen, Germany) and comprehensive metabolic panel (Siemens Vista 500). CHr (Siemens Advia) was also analyzed immediately on whole blood samples. Plasma was obtained by centrifuging the tube according to the manufacturer's specifications and then aliquoted and stored at −80°C. Frozen samples were shipped to the VitMin laboratory (Willstatt, Germany) for measurement of ferritin, CRP, AGP, sTfR, and retinol-binding protein (RBP) using a novel sandwich ELISA technique (19). Samples were also analyzed for hepcidin using the human hepcidin ELISA kit (TSZ Scientific, Waltham, MA) at Emory University as outlined by the kit's manufacturer. The average coefficient of variation of hepcidin among 11 subsamples was 18.8% (standard deviation: 18.1, range: 1.1%–64.6%).

Assessment of Nutrition and Health Status

The following thresholds were used to define abnormal values iron deficiency: ferritin <15 mg/L (20), sTFR >8.3 mg/L (21), body iron stores (BIS) <0 mg/kg, CHr <28 pg (18); vitamin A deficiency: RBP levels <0.7 mmol/L, vitamin A insufficiency: RBP levels 0.7 to <1.0 mmol/L (22); inflammation: CRP >5 mg/L (23), AGP >1.0 g/L (24). We calculated BIS for study participants using the formula based on the sTfR/ferritin ratio proposed by Cook et al (25), a measure considered to be reflective of iron status from decreased storage iron to functional tissue iron deficiency. Anemia was defined using the following thresholds for anemia per WHO guidelines (26); Hb <11.5 g/dL for children ages 5 to 11.99 years, Hb <12.0 g/dL for children ages 12 to 14.99 years, Hb <12.0 g/dL for girls ages ≥15.0 years, and Hb <13.0 g/dL for boys ages ≥15.0 years. The following data on each participant were collected: demographics (age, sex, and self-reported race/ethnicity), socioeconomic status as measured by insurance status (state provided Medicaid or private insurance), clinical disease information (disease type, duration of disease, disease location, history of prior surgeries, disease activity, prior and current medical therapy). Age was categorized into 2 groups using a cutoff of 10.0 years—this was a biologically determined cutoff for mean age of menarche in US girls using prior literature (27). Measures of anthropometrics included measurements of weight and height using standardized techniques by trained clinical nurses. Disease location was classified using the Paris classification for IBD (28). Disease activity was assessed using the following indexes: Physician Global Assessment score (29), abbreviated Pediatric Crohn Disease Activity Index (AbbrPCDAI) (30), and Pediatric Ulcerative Colitis Activity (PUCAI) (31). The abbreviated PCDAI was used in our study as growth velocity information was not available to us—this index has been reported to be comparable to the PCDAI in recent literature (28–30).

Statistical Methods

Statistical analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, NC). Statistical significance was set at a 2-sided alpha of 0.05. Descriptive statistics were presented as means (standard deviation) and as “n” (percentage) for continuous and categorical outcomes, respectively. We used the WHO Child Growth Standards (WHO Anthro, Geneva, Switzerland) to calculate age and sex adjusted z scores for anthropometric measurements. Further categorization of z scores were as follows: stunting as a height-for-age z score of <−2, wasting as BMI-for-age z score of <−2, overweight as a BMI-for-age z score of >2, and obesity as a BMI-for-age z score of >3. Underweight was defined as weight-for-age z score of <−2 and this was calculated for children younger than 10 years due to availability of WHO reference standards only in this age group. All biomarkers were examined for normality using histograms, normal probability plots, and using the Anderson-Darling test for normality. Correlations were calculated using Pearson or Spearman correlation coefficient with associated 95% confidence. The receiver operating characteristic (ROC) curves and areas under the ROC curves (AUROC) were used to estimate the prognostic values (specificity and sensitivity) of CHr and other biomarkers. Cutoff points for biomarkers were chosen to maximize sensitivity and specificity.

In the absence of the criteria standard for iron deficiency as defined by Prussian blue staining of bone marrow iron stores, we used regression modeling to adjust ferritin and sTfR for inflammation as recently recommended by our group (32) and used a combination of multiple indicators to best estimate iron deficiency, as performed in the National Health and Nutrition Examination survey in preschool-aged children (33,34). Briefly, the regression correction approach uses linear regression to adjust a given iron biomarker by the level of AGP and/or CRP on a continuous scale. The first step in the regression correction approach was to natural logarithm (ln)-transform each iron biomarker along with AGP and CRP concentrations to approximate normal distributions based on regression diagnostics. The least squares regression coefficients for AGP and/or CRP were obtained separately in a bivariate and multivariate model for each iron biomarker. The estimated slopes (ie, regression coefficients) representing the relation between AGP and/or CRP and the iron biomarker were then used to create an adjustment factor that could be added or subtracted to the biomarker based on the level of inflammation present. For sTfR, only AGP was used in the regression model based on the relation we observed and prior literature suggesting that CRP and sTfR are not correlated (35,36). Ferritin was regression corrected for both AGP and CRP. A reference value was generated to define little or no inflammation by using the 10th percentile of AGP and/or CRP concentration in our dataset to avoid overadjusting the iron biomarkers. Thus, we defined iron deficiency as an inflammation-adjusted ferritin <15 mg/L or an inflammation-adjusted sTFR >8.3 mg/L and examined the sensitivity and specificity of other biomarkers to this standard.

Access to Study Data

All authors had access to the study data and have reviewed and approved this final manuscript.


Demographics and Race

We analyzed data for 62 children ages 5 to younger than 19 years. A total of 42% (n = 26) were girls, 10% (n = 7) were stunted or wasted, and 10% (n = 7) were overweight. There were only 7 children younger than 10 years of whom none were underweight. Our population was primarily African American (n = 33, 53%) with the remaining patients being mainly Caucasian (n = 25, 40%). Demographic characteristics are summarized in Supplementary Digital Content 1, Table,

Clinical Characteristics

Our study population had predominately CD (69%, n = 43) with 31% (n = 19) having UC. Average duration of disease was 3 ± 3 years, with the majority of patients having had no prior surgery (n = 44, 71%). Classifying disease activity using Physician Global Assessment scores, most of our patients had either inactive or mild disease (n = 46, 75%) with a small number of severely ill patients (n = 4, 6%).

Inflammation, Iron Deficiency, Anemia, and Vitamin A Status

Of the iron biomarkers measured, the following were normally distributed: CHr, MCV, Hb, and RBP; and the following were non-normally distributed: ferritin, sTfR, hepcidin, RDW, and BIS. Supplementary Digital Content 2, Table, outlines the descriptive summary of our iron biomarkers. We compared the mean values of Hb, RDW, MCV, hepcidin, and iron biomarkers adjusted for inflammation (CHr, ferritin, sTfR) in children with severe compared with not severe (inactive/mild/moderate) disease in our study (Supplementary Digital Content 3, Table, and found Hb and RDW with significant differences between the groups.

Table 1 summarizes the nutritional and inflammation status of the study. Nearly half of our subjects were inflamed (elevated CRP or AGP above cutoff points) and the prevalence of anemia was 32% (n = 20). Iron deficiency prevalence varied by biomarker used, with the range of prevalence (from low to high) as follows: 11% (n = 7) using MCV <75 fL/cell, 25% (n = 15) using CHr <28 pg, 26% using BIS <0 mg/kg body weight, 39% (n = 24) using RDW >14.5%, and 52% (n = 32) using unadjusted ferritin <15 μg/L or unadjusted sTfR >8.3 mg/L. Using ferritin and sTfR, both adjusted for inflammation using regression, resulted in the highest iron deficiency prevalence estimate of 68% (n = 42). Of the patients who were iron deficient (n = 42, using ferritin <15 μg/L or sTfR >8.3 mg/L), half (50%, n = 21) were inflamed (Supplementary Digital Content 4, Table, Prevalence of iron-deficiency anemia (ferritin <15 μg/L or sTfR >8.3 mg/L, both adjusted for inflammation with Hb in anemic range) was 27% (n = 17). Of the patients who were anemic (n = 20), approximately a quarter of patients, 65% (n = 13) had duration of disease of ≥2 years and in patients with iron deficiency (n = 42), 60% (n = 25) had duration of disease of ≥2 years (Supplementary Digital Content 4, Table, and Supplementary Digital Content 5, Table, Patients with CD had a higher prevalence of both anemia (n = 15, 75%) and iron deficiency (n = 31, 74%) versus those with UC (anemia n = 5, 25%; iron deficiency n = 11, 26%) but this difference was not statistically significant. The prevalence of vitamin A deficiency was 3% (n = 2) and vitamin A insufficiency was 13% (n = 8).

Iron deficiency, inflammation, anemia, and vitamin A deficiency in the study population, n = 62

Iron Deficiency Biomarkers and Relation With Inflammation

Table 2 summarizes the Spearman correlation coefficients relating biomarkers of inflammation to iron status indicators. Our findings were notable for a strong association with both biomarkers of systemic inflammation with CHr (CRP, rs = −0.44, confidence interval [CI] = −0.62 to −0.21, P = 0.0003 and AGP, rs = −0.37, CI = −0.57 to −0.13, P = 0.003), ferritin (CRP, rs = 0.27, 95% CI = 0.02–0.49, P = 0.034 and AGP, rs = 0.36, 95% CI = 0.12–0.56, P = 0.004), BIS (CRP, rs = 0.33, 95% CI = 0.09–0.54, P = 0.008, and AGP, rs = 0.46, 95% CI = 0.24–0.64, P < 0.001), and MCV (CRP, rs = –0.41, CI = –0.60 to −0.18, P < 0.001 and AGP, rs = −0.32, CI = −0.52 to −0.07, P = 0.011). sTfR was the only iron status biomarker that showed significant correlations only with AGP as a measure of inflammation: sTfR, rs = −0.32, CI = −0.53 to −0.08, P = 0.010. Hepcidin was strongly negatively correlated with Hb (rs = −0.50; CI = −0.67 to −0.28; P < 0.0001).

Correlation coefficient matrix of biomarkers of iron status and of inflammation

Receiver Operating Characteristic Analysis

Cutoff values for optimal sensitivity and specificity were obtained using ROC curves (Fig. 2; Table 3). According to ROC curve, the optimal prognostic value for CHr to predict iron deficiency was 31 pg with an AUROC of 0.72 with 81% sensitivity and 60% specificity (Table 3). Figure 2 summarizes prognostic values of other iron biomarkers (ferritin, reticulocyte hemoglobin concentration, sTfR, Hb, red cell distribution width, mean corpuscular volume and hepcidin) according to ROC analysis. Adjusted ferritin had the highest area under the ROC with the optimal prognostic value to predict iron deficiency being 20 μg/L (sensitivity 79%, specificity 90%, AUROC 0.83). The poorest predictor of iron deficiency using the ROC was hepcidin (sensitivity 45%, specificity 60%, AUROC 0.54).

Prognostic values of iron biomarkers (ferritin, reticulocyte hemoglobin concentration, soluble transferrin receptor, hemoglobin, red blood cell distribution width, mean cell volume, hepcidin, and body iron stores) according to receiver operating characteristic curve. Lines indicate area under the curve for each biomarker. ADJ = adjusted for inflammation; AUROC = area under the receiver operating curve; BIS = body iron stores; CHr = reticulocyte hemoglobin content; FER = ferritin; Hb= hemoglobin; HEP = hepcidin; MCV = mean corpuscular volume; RDW = red blood cell distribution width; sTfR = soluble transferrin receptor; UADJ = unadjusted for inflammation.
Optimal prognostic values of different biomarkers for iron deficiency according to receiver operating characteristic curve


In this cross-sectional study, we evaluated the reticulocyte parameter CHr for diagnosis of iron deficiency in a population of children with IBD and demonstrated that CHr is affected by inflammation in children with IBD. Iron deficiency and anemia were common in this cohort of children with the prevalence of iron deficiency 68% and anemia 32% (iron-deficiency anemia 27%) after adjusting iron biomarkers data for inflammation. Interestingly, approximately two third of patients with both anemia and iron deficiency had a duration of disease ≥2 years. There was higher prevalence of anemia and iron deficiency in patients with CD (anemia n = 15; iron deficiency n = 31) versus those with UC (anemia n = 5; iron deficiency n = 11). This highlights that both iron deficiency and anemia are significantly under-recognized ongoing issues in the care of children with IBD and suggests that this remains a problem in this population even several years after diagnosis.

The diagnosis of iron deficiency is currently based on a combination of laboratory measurements: typically mature RBC measures (low MCV, low mean cell hemoglobin, increased RDW), and biochemical markers of iron metabolism (serum Fe, transferrin, transferrin saturation, ferritin, sTfR) (10,11). Of these the most reliable iron biomarkers in healthy children are ferritin and sTfR (10,11). In IBD, however, the diagnosis of iron deficiency is challenging because standard iron biomarkers are altered by inflammatory cytokines independent of changes in iron balance (7,12). Another difficult diagnostic scenario to consider is mild emerging iron deficiency in patients with IBD who are responding to therapy and are no longer anemic. Clinically, it would be helpful to be able to identify these changes earlier, to have a sensitive measure of bone marrow response to iron replacement, and to eliminate the delay to see an increase in hemoglobin concentration. A recent review examined the role of reticulocyte markers to differentiate iron-deficiency anemia from anemia of chronic disease (16). The authors reported that although there are no studies evaluating CHr in patients with IBD, it could be a reliable index for measuring the response to iron supplementation therapy. Another factor to consider is the cost of different iron biomarkers. Current practices require use of several combinations, with the total average commercial US cost (Supplementary Digital Content 6, Table, depending on which biomarkers are used. Using a single biomarker to effectively diagnose and monitor iron deficiency would substantially reduce the financial burden on the patient/insurance.

CHr has been studied as an iron biomarker in young, otherwise healthy patients (with no inflammation or chronic disease diagnoses) and has been found to be of use in screening for iron deficiency compared to other iron biomarkers such as hemoglobin and ferritin. Ullrich et al (37) investigated 202 healthy infants ages 9 to 12 months and reported that CHr of <27.5 pg is a more accurate hematological indicator of iron deficiency (defined as transferrin saturation <10%) compared with hemoglobin of <110 g/L. Brugnara et al (38) investigated 210 healthy children (mean age, 2.9 years) and reported CHr as the strongest predictor of iron deficiency (defined as transferrin saturation <20%). More recently, Kiudeliene et al (39) reported that the CHr is a comparable test with ferritin and transferrin saturation and can be used to detect iron deficiency (diagnosed when at least 2 of 4 parameters [ferritin <12 μg/L, transferrin >3.6 g/L, transferrin saturation <10%, sTfR >1.8 mg/L] were abnormal) in a cohort of 180 healthy children ages 6 to 24 months. An adult study investigated CHr in 78 adult patients undergoing bone marrow examination as part of an anemia work-up that included benign hematologic disorders and malignancies. They demonstrated that CHr had the highest overall sensitivity and specificity when compared with ferritin, transferrin saturation, and MCV for predicting iron deficiency (defined as lack of stainable iron in bone marrow aspirate) after exclusion of patients with MCV of >100 fL/cell and thalassemia (18).

Our study is the first to date reporting the use of CHr in pediatric patients with IBD, and we showed that CHr was correlated with inflammation (as measured by CRP and AGP). Our findings are similar to results reported by Hackeng et al (40) in a population of adult patients undergoing dialysis, showing that CHr was significantly and inversely related to log CRP (rho = −0.50; P < 0.0001). We found that CHr was strongly correlated with both CRP and AGP, possibly due to the high prevalence of both acute and chronic inflammation in our study population (nearly 1 in 2 children had elevated CRP or AGP). Furthermore, as indicated by the high hepcidin values, anemia of chronic disease may have played a role in sequestering iron and thus decreasing CHr levels (41). We showed sTfR to be negatively correlated with inflammation as measured by AGP, which is contrary to prior literature that has shown a positive association between inflammation and AGP (42,43). In populations with acute inflammation, sTfR has a positive relation with AGP and hence adjusting it would decrease estimated prevalence of iron deficiency. We hypothesize that the opposite correlation in our study may be due to the underlying chronic inflammation in our IBD population that may thus blunt erythropoiesis, and thus sTfR concentrations (44,45).

One of the secondary aims of the study was to determine the diagnostic capacity (sensitivity and specificity) of CHr as a biomarker of iron deficiency. The following cutoffs for low CHr to measure iron deficiency had high sensitivity, but moderate specificity: 31 pg unadjusted and 34 pg inflammation-adjusted. Both of these are much higher than other reported cutoffs in the literature: 24 pg in a population of elderly anemic patients (46); 28 pg compared with bone marrow iron stores in adults (18); and 27.5 pg compared with hemoglobin in healthy infants (37). We think that our higher cutoffs both for unadjusted and adjusted CHr could be related to not comparing to bone marrow iron as the ROC “criterion standard” and the high prevalence of chronic inflammation in our study population.

Adjusted ferritin had the highest sensitivity (79%) and specificity (90%) to predict iron deficiency at a threshold value of 20 μg/L and an area under the ROC of 0.83. It is important to note, however, that both inflammation-adjusted ferritin and sTfR were part of the iron deficiency criterion standard for the ROC analysis. Interestingly, contrary to work by Basseri et al (15), we showed our poorest iron biomarker to be hepcidin (sensitivity 44%, specificity 54%, AUROC 0.51). We had, similar to their group, excluded patients who were receiving iron supplementation or had an MCV of >100 fL/cell as potentially having B12 or folate deficiency. Our sample size was, however, much larger (n = 62 vs n = 17); we also included patients with both UC and CD (compared with CD alone) and excluded patients with a recent history of receiving a packed RBC transfusion—these methodological differences could account for our different results. In summary, our analysis showed that all clinically available iron biomarkers are affected by inflammation. One consideration is that different biomarkers reflect different stages and/or types of iron deficiency and will therefore have different prognostic values.

The strengths of the study were as follows: use of regression adjustment of iron biomarkers for use in the ROC analysis; stratified enrollment to ensure a significant proportion of patients with inflammation (and iron deficiency) across the IBD spectrum; evaluation of alternate cutoffs for CHr; and measurement of hepcidin which has been proposed to be a measure of anemia of chronic disease. Limitations of the study include the cross-sectional design, preventing causal associations; the relatively small cohort of children; exclusion of individuals with an MCV of >100 fL/cell and the lack of vitamin B12 status measures and menstrual history.

In summary, the present study highlights the high prevalence of iron deficiency and anemia in the care of children with IBD. Our evaluation of CHr as a diagnostic test showed it to be no better than the current standard iron biomarkers. All explored iron biomarkers were affected by inflammation and should be adjusted. A single iron biomarker is unlikely to best predict iron deficiency in pediatric IBD. Iron intervention studies are needed to examine the response of iron biomarkers to iron supplementation in the setting of inflammation and to further evaluate the risk-benefit of iron supplementation in inflamed individuals. Validation of adjusting for the effect of inflammation on iron biomarkers using a regression approach merits further investigation and will help to identify children who may be suitable for therapeutic intervention which in turn may make significant improvements in quality of life.


This work could not have been completed without the invaluable input of the Kugathasan Lab IBD Dream Team: Kari Aldridge, Corinthian Bryant, Bernadette Martineau, David T. Okou, and Mahadev Prasad. The authors would also like to thank our clinical team whose help in patient recruitment was critical in the success of this project: Cary G. Sauer, Barbara O. McElhanon, Gail Tenjarla, Walter Ifeadike, Christine Spainhour, Brit Eyster, and Lisa Mitchell. Finally, the authors would also like to acknowledge the support of Janet Gross and Jose Binongo who helped review earlier versions of the proposal.


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anemia; children; Crohn disease; inflammation; iron deficiency; ulcerative colitis

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