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Iron deficiency in early pregnancy using serum ferritin and soluble transferrin receptor concentrations are associated with pregnancy and birth outcomes




There are several biomarkers for measuring iron deficiency (ID) in pregnancy, but the prevalence of ID and its association with inflammation and adverse pregnancy outcomes is inconclusive. The aim of this work was to describe the prevalence and determinants of first trimester ID and associations with pregnancy and birth outcomes.


A record-linkage cohort study of archived serum samples of women attending first trimester screening and birth and hospital data to ascertain maternal characteristics and pregnancy outcomes. Sera were analysed for iron stores (ferritin; μg/l), lack of iron in the tissues (soluble transferrin receptor (sTfR); nmol/l) and inflammatory (C-reactive protein (CRP); mg/dl) biomarkers. Total body iron (TBI) was calculated from serum ferritin (SF) and sTfR concentrations. Multivariate logistic regression analysed risk factors and pregnancy outcomes associated with ID using the definitions: SF<12 μg/l, TfR21.0 nmol/l, and TBI<0 mg/kg.


Of the 4420 women, the prevalence of ID based on ferritin, sTfR and TBI was 19.6, 15.3 and 15.7%, respectively. Risk factors of ID varied depending on which iron parameter was used and included maternal age <25 years, multiparity, socioeconomic disadvantage, high maternal body weight and inflammation. ID, defined by SF and TBI but not TfR, was associated with reduced risk of gestational diabetes mellitus (GDM). ID defined using TBI only was associated with increased risk of large-for-gestation-age (LGA) infants.


Nearly one in five Australian women begin pregnancy with ID. Further investigation of excess maternal weight and inflammation in the relationships between ID and GDM and LGA infants is needed.


It is well established worldwide that women are at increased risk of iron deficiency (ID) during pregnancy.1 Pregnant women with serum ferritin (SF) concentrations <12 μg/l are classified as ID and having depleted iron stores.2 SF is maximum at 12–16 weeks of gestation and then falls with advancing gestation from haemodilution and mobilization of iron stores.3 Because of these normal physiological changes in SF concentration in later pregnancy, it has been suggested that the best time to detect maternal ID is in early pregnancy.1

There is, however, an inherent difficulty in interpreting SF concentrations because ferritin takes part in the systemic acute phase response and can increase markedly in the presence of acute or chronic infection.4 To aid the interpretation of ferritin concentration, concurrent measurement of an acute phase response protein, which is most commonly C-reactive protein (CRP), is recommended.2 Measurement of circulating soluble transferrin receptor (sTfR) concentrations are also reported to be useful for defining ID because studies have found that sTfR may not be affected by infection2, 5 or may be affected but to a lesser degree than SF.6 sTfR suffers from a lack of standardization of the method and significant variation in the references ranges used.7 Another method proposed for evaluating iron status within a population is the estimation of total body iron (TBI) on the basis of the ratio of sTfR to SF.8

There are limited data on the prevalence of ID using these multiple iron indices from large, population-based studies.9 Although there is some evidence that maternal ID is associated with increased risk of preterm delivery10 and low birth weight,11 reviews and meta-analyses are inconclusive as to whether iron supplementation in pregnancy improves clinical outcomes for the mother or infant.9, 12, 13 Therefore, the aims of this study are to examine the prevalence of ID in women in the first trimester of pregnancy using various measures of iron status of SF, sTfR, TBI and CRP and assess risk factors of ID and associations between ID and pregnancy and birth outcomes.

Subjects and methods

Study population

This cohort study included a random sample of pregnant women who attended first trimester Down syndrome screening between January and October 2007 and had their results analysed by Pathology North, a state-wide public screening service in New South Wales, Australia. For this study, archived serum samples were thawed and analysed for SF (μg/l), sTfR (nmol/l) and CRP (mg/dl). SF was measured using a solid-phase direct sandwich enzyme-linked immunosorbent assay (ELISA) method (Calbiotech, Inc., Spring Valley, CA, USA) with an inter-assay coefficient of variation of 6.2%. sTfR concentrations were measured using an ELISA (Quantikine IVD, Human sTfR Immunoassay, R & D Systems, Minneapolis, MN, USA) with an inter-assay coefficient of variation of 6.4%. TBI (mg/kg) was calculated using the formula: −(log10 (sTfR/ferritin)−2.8229)/0.1207.5, 8 Positive values of TBI represent storage iron and negative values indicate a deficient iron supply to peripheral tissues.8 CRP was measured using the quantitative sandwich enzyme immunoassay technique (Quantikine) with an inter-assay coefficient of variation of 13.3%. Three established definitions for ID were used: SF<12 μg/l,2 TfR 21.0 nmol/l, according to the manufacturer’s guidelines,14 and TBI<0 mg/kg.5, 8 In addition, SF concentration >70 μg/l was used to define iron-replete women with adequate iron reserves to meet the estimated iron requirement of pregnancy.15

Data sources

The laboratory database provided data on maternal body weight and gestational age at the time of screening. Information from the laboratory database and biomarker concentrations analysed using each woman’s serum were linked to birth and hospital records. Birth data were sourced from the NSW Perinatal Data Collection (PDC) and hospitalization data from the NSW Admitted Patients Data Collection (APDC). The PDC is a statutory population-based collection of all births in NSW of at least 400-g birth weight or at least 20 weeks of gestation and includes information on maternal characteristics, pregnancy, labour, delivery and infant outcomes at birth. The APDC is a census of all admissions in NSW public and private hospitals. Up to 50 diagnosis and procedures for each separation are coded according to the tenth revision of the International Classification of Diseases, Australian Modification.16 The NSW Centre for Health Record Linkage (CHeReL) performed probabilistic record linkage.17 The CHeReL assesses linkage quality and for this study reported <5/1000 missed links and <2/1000 false positive links. Only de-identified data were provided to the researchers. The study was approved by the NSW Population and Health Services Research Ethics Committee.

Only maternal, pregnancy and obstetric factors known to be reliably reported in birth and/or hospital data were included in the analysis.18, 19 Explanatory variables included maternal age, parity, smoking during pregnancy and type of hospital (private versus public). Postcode was used to derive an indicator of socioeconomic status (SES). An Index of Relative Disadvantage produced by the Australian Bureau of Statistics was assigned to each postcode and women in the lowest 20th percentile were classified as disadvantaged.20 Pregnancy outcomes included gestational diabetes mellitus (GDM), hypertensive disorders in pregnancy, postpartum haemorrhage, stillbirth, preterm birth, infant birthweight, small for gestational age, large for gestational age (LGA) and infant admission to neonatal intensive or special care unit. GDM was identified from hospital data based on diagnosis by the attending clinician.19, 21, 22 Hypertensive disorders in pregnancy included women with the onset of hypertension from 20 weeks, including gestational hypertension, preeclampsia and eclampsia.23 Postpartum haemorrhage was defined as blood loss of 500 ml following vaginal birth or 750 ml following caesarean section19 and where a diagnosis of postpartum haemorrhage was recorded in the medical record. Stillbirth (in utero fetal death after 20 weeks of gestation), preterm birth (<37 weeks gestation), infant birth weight and infant admission to a neonatal intensive or special care unit were identified from PDC data. Small for gestational age and LGA were defined, respectively, as those infants in the <10th percentile and >90th percentile birth weight distribution for gestational age and infant sex.24

Statistical analysis

The prevalence of ID was calculated using established definitions for ferritin, sTfR and TBI. The concentrations of these nonparametric iron biomarkers including and then excluding women with elevated CRP (>95th centile, >0.5 mg/dl) are described using the median and 25 and 75th percentiles. Univariate analysis were performed to examine the association between maternal characteristics and pregnancy and birth outcomes with each of the three definitions of ID, using ferritin, sTfR and TBI, using Chi-squared (X2) test, or in the case of small cell sizes, Fisher’s exact test. Multivariate logistic regression analysis was performed to account for potential confounders. Using backward stepwise selection, variables with least significance were progressively dropped from each model until all remaining covariates were statistically significant (two-tailed P<0.05). Variables not selected were then added back into the selected model, one at a time to assess whether they were confounders (that is, changed the effect by >10%), and final model was determined. Statistical analysis was performed using SAS for Windows version 9.3 (SAS Institute Inc., Cary, NC, USA).


Sample characteristics

A total of 4420 women were included in the analysis after excluding 122 women with a twin pregnancy, medical abortion, infant with a major congenital anomaly or an undetectable ferritin and sTfR concentration. The mean (±s.d.) age of women was 32.2 (±4.9) years (8.0% <25 years), 36.5% were birthed in private hospitals and 35.8% of women were classified as disadvantaged. Nearly half (51.5%) of the women were nulliparous and 5.8% smoked during pregnancy. At the time of testing, mean (±s.d.) gestational age was 12.0 (±1.0) weeks and 50.8% were at 10–12 weeks of gestation. The mean (±s.d.) maternal body weight at the time of testing was 67.0 (±14.4) kg and maternal body weight in the 75th percentile was defined as 73 kg.

Prevalence and risk factors of ID

The prevalence of ID based on ferritin, sTfR and TBI measures was 19.6, 15.3 and 15.7%, respectively (Table 1). A small proportion of women (15.3%) were defined as ID using all three iron parameters. Of the 4006 women with detectable CRP values, 65.3% had CRP levels >0.5 mg/dl, an indication of inflammation. When women with CRP>0.5 mg/dl were excluded, there were moderate changes in the prevalence of ID (Table 1). Excluding women with elevated CRP levels, only 9.7% of women (n=234/2407) had SF>70 μg/l, indicating adequate iron reserves to meet the increased iron requirements of pregnancy.

Table 1 Description of median, 25th and 75th percentiles and proportion of iron-deficient women based on first trimester serum ferritin, transferrin receptor and total body iron measurements for all women and for women without inflammation (n=4420)

Descriptive statistics for maternal risk factors and pregnancy outcomes and univariate association with various measures of ID are presented in Table 2. After adjusting for important confounders in multivariate analyses, univariate association of maternal risk factors with ID as defined using SF, TBI and sTfR remained. Specifically, women with SF<12 μg/l were significantly more likely to be younger with maternal age <25 years (adjusted odds ratio (AOR): 2.24; 95% confidence interval (CI): 1.68, 2.95, P<0.001), of low SES (AOR: 1.30; 95% CI: 1.09, 1.55, P=0.004) and multiparous (AOR: 1.67; 95% CI: 1.40, 1.99, P<0.001). Using sTfR, multivariate analyses found that women with ID were more likely to be multiparous (AOR: 1.41, 95% CI: 1.16, 1.71, P<0.001), of low SES (AOR: 1.37; 95% CI: 1.13, 1.67, P=0.002), have higher CRP levels (AOR: 1.40; 95% CI: 1.26, 1.55, P<0.0001) and less likely to smoke during pregnancy (AOR: 0.48; 95% CI: 0.31, 0.77, P=0.002). TBI-defined ID was associated with maternal age <25 years (AOR: 2.18, 95% CI: 1.60, 2.90, P<0.001) and multiparous births (AOR: 1.52; 95% CI: 1.24, 1.87, P<0.001).

Table 2 Univariate analysis of maternal and pregnancy characteristics and pregnancy and birth outcomes by different definitions of maternal iron deficiency using serum ferritin, soluble transferrin receptor and total body iron concentrations

For pregnancy and birth outcomes, ID defined using SF or TBI was significantly associated with decreased odds of GDM and increased odds of LGA infants (Table 2). Women with hypertensive disorders in pregnancy were more likely to have high sTfR ID in early pregnancy (Table 2). Multivariate analyses found that early pregnancy ID defined using SF or TBI remained significantly associated with reduced odds of GDM (AOR 0.43; 95% CI 0.23, 0.78 and AOR 0.39; 95% CI 0.20, 0.78, respectively; Table 3). None of the other covariates remained in final models except for CRP levels, which was positively associated with GDM for ID defined using SF (AOR 1.32; 95% CI 1.11, 1.57) and TBI (AOR 1.34; 95% CI 1.13, 1.59).

Table 3 Multivariate analysis of pregnancy and birth outcomes by different definitions of maternal iron deficiency using serum ferritin, soluble transferrin receptor and total body iron concentrations

ID defined using TBI (AOR 1.38; 95% CI 1.03, 1.85) but not SF (AOR 1.25; 95% CI 0.95, 1.65) remained significantly associated with increased odds of LGA infants. In the final model or ID defined using TBI and LGA, increased maternal weight (AOR 2.75; 95% CI 2.17, 3.48), multiparity (AOR 1.95; 95% CI 1.53, 2.48) and smoking during pregnancy (AOR 0.36; 95% CI 0.18, 0.72) remained significant factors associated with increased odds of LGA infants. Finally, for ID defined using sTfR, multivariate analyses found ID was no longer significantly associated with hypertensive disorders in pregnancy (AOR: 1.20. 95% CI: 0.88, 1.88, P=0.18) or LGA infants (AOR: 0.86, 95% CI: 0.64, 1.16, P=0.32).


Results indicate that up to one in five Australian women enter pregnancy with ID, and only 11% begin pregnancy with sufficient iron stores to meet the total estimated iron requirements of pregnancy. The prevalence and risk factors of ID varied depending on which iron parameter was used to define ID. Depleted iron stores, as defined by low SF, occurred in 20% of women and were associated with being younger, multiparous and more socioeconomically disadvantaged. The prevalence of ID using sTfR and TBI were both around 15% and associated with high maternal body weight, multiparity and inflammation. In terms of the consequences of maternal ID, this study found that women with first trimester ID were less likely to develop GDM and more likely to have LGA infants, based on the iron parameters SF and TBI but not sTfR.

There are no data from Australian studies reporting on the prevalence of ID in pregnant women with which to compare our results. The prevalence of ID in pregnant women varies worldwide from 25% to >90%,25, 26, 27 depending on the study population and iron supplement practices. Similar to the current study, others have found differences in the prevalence and risk factors for ID depending on which iron measure is used to define ID.7, 27, 28, 29, 30 The present study found that 15.3% of women were defined as ID using all three definitions. Although the three iron indicators showed relatively good agreement for ID prevalence and identified similar groups at highest risk of ID, the different estimates of ID prevalence demonstrates that different iron measures reflect a slightly different aspect of iron metabolism.28 Ferritin concentrations reflect decreased storage iron but are insensitive to further change during severe ID or negative iron balance.5 sTfR concentrations reflect functional tissue ID and generally begin to change only after iron stores (in the form of ferritin) are depleted.5 sTfR is also elevated by ineffective erythropoiesis.14 Most women with ID based on sTfR levels (70%) had adequate iron stores (SF levels 12 μg/l), possibly indicating impaired erythropoietin production as a result of an immune response by inflammatory cytokines31 rather than inadequate iron nutrition.

Multivariate analyses found women with ID based on SF and TBI concentrations were more likely to be younger, multiparous and socioeconomically disadvantaged. These risk factors of ID have previously been reported elsewhere.9, 27, 30 Multiparity may reflect depleted iron supply with increasing pregnancies, while younger age and low SES are thought to reflect poorer diets and lower intake of dietary iron and supplements. For women with ID defined using sTfR, multiparity and low SES were also risk factors. However, these women were also more likely to be heavier and have high CRP concentrations. Postulated explanations for the association between greater maternal weight, a marker of obesity and ID include dilutional hypoferremia, poor dietary iron intake, increased iron requirements, and/or impaired iron absorption in obese individuals.32 There is also recent evidence that obesity-related inflammation may have a role through its regulation of hepcidin, such that iron absorption is reduced.32

In terms of pregnancy and birth outcomes, our study did not detect a significant association between ID and preterm birth or small-for-gestational-age infants. Previous literature on the association between ID and these pregnancy outcomes is inconsistent,12, 13, 33 with some reporting no association between ID and preterm birth,11 while others have found an association with low ferritin.34, 35 Inconsistencies in the literature may be explained by studies conducted in high-risk or different populations and/or settings and lack of adjustment for important confounders such as body weight and low-grade inflammation in analyses.

Women with ID defined using SF and TBI were less likely to develop subsequent GDM. These findings confirm those by Lao and Ho,36 who in a retrospective study of 242 pregnant women with ID found that women with ID anaemia were less likely to have GDM (AOR: 0.46; 95% CI: 0.23, 0.90) after adjusting for multiparity and BMI25 kg/m2. Women with ID anaemia had significantly lower gestational weight gain throughout pregnancy, which the authors interpreted as suggestive of lower dietary energy and iron intakes. One of the limitations of the study by Lao and Ho36 is the lack of data on inflammatory biomarkers. In our study, increased CRP levels, suggestive of increased inflammation, were significantly associated with increased odds of GDM. GDM is increasingly being recognized as an inflammatory condition that involves unbalanced inflammatory cytokine production.37 An important component of innate immunity during infection and inflammation is redistribution of iron, whereby iron is shifted from the circulation into cellular stores to decrease iron bioavailability to invading microorganisms.38 Being iron deficient may offer some advantage in that less iron is available to invading pathogens. Redistribution of iron from the circulation into cellular stores may explain why an association with GDM was only found for SF and TBI and not sTfR. It is uncertain whether elevated SF concentrations reflect excess iron or inflammation and further studies are needed to elucidate this pathway.

The finding that ID was associated with increased risk of LGA is also consistent with Lao et al.39 We found excessive maternal body weight but not CRP levels to be associated with LGA, suggesting that the previously stated explanations for the association between ID and obesity, poor dietary iron intake or impaired iron absorption in obese individuals may be contributing.32 It could also be that CRP levels were not associated with having an LGA infant because they were measured early in pregnancy. It is possible that CRP levels may have changed later in pregnancy and showed evidence of inflammation. It is also possible that women in our study with first trimester ID were diagnosed at their first antenatal booking and recommended to take iron supplements. A recent systematic review and meta-analysis of iron supplement use in pregnancy found that a daily dose of iron was associated with a significant increase in birth weight and decrease in risk of low birth weight.33 However, without information on iron supplement use or the iron status of these women later in pregnancy, it is uncertain whether improved maternal iron status led to increased risk of LGA infants among these women.

Strengths of this study are the large population-based cohort design with thorough measurement, high ascertainment and reporting of first trimester iron status and pregnancy and birth outcomes and examination of a combination of biomarkers of ID, which provide information on different stages of ID. Limitations include lack of data on iron supplement use, maternal diet, maternal insulin and glucose concentrations and infant iron status. A notable limitation is the lack of data on maternal anaemia. Data from previous studies suggest that increased haemoglobin is associated with increased risk of glucose intolerance, diabetes and GDM,40, 41, 42 and one study which found no difference in the incidence of GDM between anaemic and non-anaemic women found women with ID anaemia had about one-half the incidence of GDM compared with non-anaemic women.43 The relationship between anaemia and GDM is not well understood and requires further study. A notable study limitation is the lack of assay standardization for sTfR. Quantification of sTfR is obtained from immunological methods such as ELISA, immunonephelometry and immunoturbidimetry. Values obtained from different methods are not comparable, which is a limitation of sTfR data.44 The TBI prediction equation was developed using sTfR data obtained with an in-house ELISA developed by Flowers et al.,45 which served as the basis for the development of the Ramco assay and may not be directly applicable to studies using sTfR data measured with other types of assays. Although the present study used an ELISA assay to obtain sTfR values, use of a different ELISA kit to the one used by Flowers et al.45 to establish the body iron model may have influenced the body iron values obtained in the present study.

In conclusion, a significant proportion of women experience early pregnancy ID reinforcing the importance of routine screening of pregnant women for anaemia and performing iron studies among those suspected of ID. More research is needed to understand how to best interpret information from multiple iron measurements taking into consideration the complex changes that occur in these concentrations during pregnancy. The association between ID and decreased risk of GDM and increased risk of LGA requires further examination in other study populations.


  1. 1

    Scholl T . Iron status during pregnancy: setting the stage for mother and infant. Am J Clin Nutr 2005; 81: S1218–S1222.

    Article  Google Scholar 

  2. 2

    WHO. Serum ferritin concentrations for the assessment of iron status and iron deficiency in populations. Vitamin and Mineral Nutrition Information System Geneva, World Health Organization, (WHO/NMH/NHD/MNM/112), 2011: (accessed 01 August 2014).

  3. 3

    Milman N, Agger AO, Nielsen OJ . Iron status markers and serum erythropoietin in 120 mothers and newborn infants. Acta Obstet Gynecol Scand 1994; 73: 200–204.

    CAS  Article  Google Scholar 

  4. 4

    Wish J . Assessing iron status: beyond serum ferritin and transferrin saturation. Clin J Am Soc Nephrol 2006; 1: S4–S8.

    CAS  Article  Google Scholar 

  5. 5

    Skikne B, Flowers CH, Cook JD . Serum transferrin receptor: a quantitative measure of tissue iron deficiency. Blood 1990; 75: 1870–1876.

    CAS  PubMed  Google Scholar 

  6. 6

    Righetti A, Wegmüller R, Glinz D, Ouattara M, Adiossan LG, N'Goran EK et al. Effects of inflammation and Plasmodium falciparum infection on soluble transferrin receptor and plasma ferritin concentration in different age groups: a prospective longitudinal study in Côte d'Ivoire. Am J Clin Nutr 2013; 97: 1364–1374.

    CAS  Article  Google Scholar 

  7. 7

    Leonard A, Patterson AJ, Chalmers K, Collins CE . Is soluble transferrin receptor a useful marker in early stage iron deficiency? e-SPEN Journal 2013; 8: e210–e212.

    Article  Google Scholar 

  8. 8

    Cook J, Flowers CH, Skikne BS . The quantitative assessment of body iron. Blood 2003; 101: 3359–3364.

    CAS  Article  Google Scholar 

  9. 9

    Pavord S, Myers B, Robinson S, Allard S, Strong J, Oppenheimer C . British Committee for Standards in Haematology. UK guidelines on the management of iron deficiency in pregnancy. Br J Haematol 2012; 156: 588–600.

    CAS  Article  Google Scholar 

  10. 10

    Haider B, Olofin I, Wang M, Spiegelman D, Ezzati M, Fawzi WW . Anaemia, prenatal iron use, and risk of adverse pregnancy outcomes: systematic review and meta-analysis. BMJ 2013; 346: 3443.

    Article  Google Scholar 

  11. 11

    Cogswell M, Parvanta I, Ickes L, Yip R, Brittenham GM . Iron supplementation during pregnancy, anemia, and birth weight: a randomized controlled trial. Am J Clin Nutr 2003; 78: 773–781.

    CAS  Article  Google Scholar 

  12. 12

    US Preventive Services Task Force. Routine iron supplementation during pregnancy. JAMA 1993; 270: 2848–2854.

    Article  Google Scholar 

  13. 13

    Rasmussen K . Is there a causal relationship between iron deficiency or iron-deficiency anemia and weight at birth, length of gestation and perinatal mortality? J Nutr 2001; 131: 590 S–601 S.

    Article  Google Scholar 

  14. 14

    Human sTfR Immunoassay Quantikine IVD Soluble Transferrin Receptor ELISA R&D Systems Inc. Catalog Number DTFR1 2013; R&D Systems China Co. Ltd.

  15. 15

    Milman N . Iron in pregnancy: how do we secure an appropriate iron status in the mother and child? Ann Nutr Metab 2011; 59: 50–54.

    CAS  Article  Google Scholar 

  16. 16

    Wen S, Smith G, Yang Q, Walker M . Epidemiology of preterm birth and neonatal outcome. Semin Fetal Neonatal Med 2004; 9: 429–435.

    Article  Google Scholar 

  17. 17

    Lawrence G, Dinh I, Taylor L . The centre for health record linkage: a new resource for health services research and evaluation. HIM J 2008; 37: 60–62.

    Article  Google Scholar 

  18. 18

    Roberts C, Cameron CA, Bell JC, Algert C, Morris J . Measuring maternal morbidity in routinely collected health data: development and validation of a maternal morbidity outcome indicator. Med Care 2008; 46: 786–794.

    Article  Google Scholar 

  19. 19

    Taylor L, Travis S, Pym M, Olive E, Henderson-Smart DJ . How useful are hospital morbidity data for monitoring conditions occurring in the perinatal period? Aust N Z J Obstet Gynaecol 2005; 45: 36–41.

    Article  Google Scholar 

  20. 20

    Australian Bureau of Statistics Socio-Economic Indexes for Areas (SEIFA), Technical Paper, 2008 2039:0.55.00.

  21. 21

    Bell J, Ford JB, Cameron CA, Roberts CL . The accuracy of population health data for monitoring trends and outcomes among women with diabetes in pregnancy. Diabetes Res Clin Pract 2008; 81: 105–109.

    Article  Google Scholar 

  22. 22

    Lain S, Hadfield RM, Raynes-Greenow CH, Ford JB, Mealing NM, Algert CS . Quality of data in perinatal population health databases: a systematic review. Med Care 2002; 50: e7–20.

    Article  Google Scholar 

  23. 23

    Brown M, Lindheimer MD, de Swiet M, Van Assche A, Moutquin JM . The classification and diagnosis of the hypertensive disorders of pregnancy: statement from the International Society for the Study of Hypertension in Pregnancy (ISSHP). Hypertens Pregnancy 2001; 20: IX–XIV.

    CAS  Article  Google Scholar 

  24. 24

    Roberts C, Lancaster PA . Australian national birthweight percentiles by gestational age. Med J Aust 1999; 170: 114–118.

    CAS  Article  Google Scholar 

  25. 25

    Hercberg S, Preziosi P, Galan P . Iron deficiency in Europe. Public Health Nutr 4 2001; 4: 537–545.

    CAS  Article  Google Scholar 

  26. 26

    Lee J, Lee JH, Ahn S, Kim JW, Chang H, Kim YJ et al. Prevalence and risk factors for iron deficiency anemia in the korean population: results of the fifth Korea National Health and Nutrition Examination Survey. J Korean Med Sci 2014; 29: 224–229.

    CAS  Article  Google Scholar 

  27. 27

    Mei Z, Cogswell ME, Looker AC, Pfeiffer CM, Cusick SE, Lacher DA et al. Assessment of iron status in US pregnant women from the National Health and Nutrition Examination Survey (NHANES), 1999–2006. Am J Clin Nutr 2011; 93: 1312–1320.

    CAS  Article  Google Scholar 

  28. 28

    Engle-Stone R, Nankap M, Ndjebayi AO, Erhardt JG, Brown KH . Plasma ferritin and soluble transferrin receptor concentrations and body iron stores identify similar risk factors for iron deficiency but result in different estimates of the national prevalence of iron deficiency and iron-deficiency anemia among women and children in Cameroon. J Nutr 2013; 143: 369–377.

    CAS  Article  Google Scholar 

  29. 29

    Knowles J, Thurnham DI, Phengdy B, Houamboun K, Philavong K, Keomoungkhone I et al. Impact of inflammation on the biomarkers of iron status in a cross-sectional survey of Lao women and children. Br J Nutr 2013; 110: 2285–2297.

    CAS  Article  Google Scholar 

  30. 30

    Vandevijvere S, Amsalkhir S, Van Oyen H, Egli I, Moreno-Reyes R . Iron status and its determinants in a nationally representative sample of pregnant women. J Acad Nutr Diet 2013; 113: 659–666.

    Article  Google Scholar 

  31. 31

    Andrews N . Anemia of inflammation: the cytokine-hepcidin link. J Clin Invest 2004; 113: 1251–1253.

    CAS  Article  Google Scholar 

  32. 32

    Cepeda-Lopez A, Aeberli I, Zimmermann MB . Does obesity increase risk for iron deficiency? A review of the literature and the potential mechanisms. Int J Vitam Nutr Res 2010; 80: 263–270.

    CAS  Article  Google Scholar 

  33. 33

    Knudsen V, Hansen HS, Ovesen L, Mikkelsen TB, Olsen SF . Iron supplement use among Danish pregnant women. Public Health Nutr 2007; 10: 1104–1110.

    Article  Google Scholar 

  34. 34

    Allen L . Anemia and iron deficiency: effects on pregnancy outcome. Am J Clin Nutr 2000; 71: 280 s–284.

    Article  Google Scholar 

  35. 35

    Siega-Riz A, Hartzema AG, Turnbull C, Thorp J, McDonald T, Cogswell ME . The effects of prophylactic iron given in prenatal supplements on iron status and birth outcomes: a randomized controlled trial. Am J Obstet Gynecol 2006; 194: 512–519.

    CAS  Article  Google Scholar 

  36. 36

    Lao T, Ho LF . Impact of iron deficiency anemia on prevalence of gestational diabetes mellitus. Diabetes Care 2004; 27: 650–656.

    Article  Google Scholar 

  37. 37

    Milman N, Bergholt T, Eriksen L, Byg KE, Graudal N, Pedersen P et al. Iron prophylaxis during pregnancy - How much iron is needed? A randomized dose-response study of 20–80 mg ferrous iron daily in pregnant women. Acta Obstet Gynecol Scand 2005; 84: 238–247.

    Article  Google Scholar 

  38. 38

    Rioux F, LeBlanc CP . Iron supplementation during pregnancy: what are the risks and benefits of current practices? Appl Physiol Nutr Metab 2007; 32: 282–288.

    CAS  Article  Google Scholar 

  39. 39

    Lao T, Tam KF, Chan LY . Third trimester iron status and pregnancy outcome in non-anaemic women; pregnancy unfavourably affected by maternal iron excess. Hum Reprod 2000; 15: 1843–1848.

    CAS  Article  Google Scholar 

  40. 40

    Lao TT, C LY, Tam KF, Ho LF . Maternal hemoglobin and risk of gestational diabetes mellitus in Chinese women. Obstet Gynecol 2002; 99: 807–812.

    CAS  PubMed  Google Scholar 

  41. 41

    Rao G, Morghom LO . Erythrocyte and hemoglobin levels in diabetic women. Eur J Appl Physiol 1984; 52: 272–275.

    CAS  Article  Google Scholar 

  42. 42

    Harlan L, Harlan WR, Landis JR, Goldstein NG . Factors associated with glucose tolerance in adults in the United States. Am J Epidemiol 1987; 126: 674–684.

    CAS  Article  Google Scholar 

  43. 43

    Lao T, Pun TC . Anaemia in pregnancy: is the current definition meaningful? Eur J Obstet Gynecol Reprod Biol 1996; 68: 53–58.

    CAS  Article  Google Scholar 

  44. 44

    Pfeiffer C, Cook JD, Mei Z, Cogswell ME, Looker AC, Lacher DA . Evaluation of an automated soluble transferrin receptor (sTfR) assay on the Roche Hitachi analyzer and its comparison to two ELISA assays. Clin Chim Acta 2007; 382: 112–116.

    CAS  Article  Google Scholar 

  45. 45

    Flowers C, Skikne BS, Covell AM, Cook JD . The clinical measurement of serum transferrin receptor. J Lab Clin Med 1989; 114: 368–377.

    CAS  PubMed  Google Scholar 

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We thank the New South Wales PaLMS Pathology service and Ministry of Health for provision of population data and the NSW Centre for Health Record Linkage for record linkage. This work was funded by a National Health and Medical Research Council (NHMRC) Project Grant (no. 632653). Funding for Amina Khambalia is by an Australian NHMRC Centers for Research Excellence (APP1001066), to Natasha Nassar by an NHMRC Career Development Fellowship (no. APP1067066) and to Christine Roberts by an NHMRC Senior Research Fellowship (no. APP1021025). Clare Collins is supported by a Faculty of Health and Medicine Strategic Research Fellowship at the University of Newcastle.

Author contributions

AZK, NN, CLR, JM and VT conceived and designed the study; NN, CLR, JM and VT acquired data; AZK was responsible for the integrity of data and statistical analysis; AZK drafted the manuscript; and all authors approved the manuscript and critically reviewed the manuscript for important intellectual content.

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Correspondence to A Z Khambalia.

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Khambalia, A., Collins, C., Roberts, C. et al. Iron deficiency in early pregnancy using serum ferritin and soluble transferrin receptor concentrations are associated with pregnancy and birth outcomes. Eur J Clin Nutr 70, 358–363 (2016).

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