|Year : 2018 | Volume
| Issue : 21 | Page : 2566-2574
Association between Maternal Weight Indicators and Iron Deficiency Anemia during Pregnancy: A Cohort Study
Jing Tan1, Ya-Na Qi1, Guo-Lin He2, Hong-Mei Yang2, Gui-Ting Zhang1, Kang Zou1, Wei Luo3, Xin Sun1, Xing-Hui Liu2
1 Chinese Evidence-Based Medicine Center and CREAT Group, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China
2 Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610000, China
3 School of Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610000, China
|Date of Submission||09-Jul-2018|
|Date of Web Publication||25-Oct-2018|
Prof. Xing-Hui Liu
Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610000
Source of Support: None, Conflict of Interest: None
Background: The effect of maternal weights on the risk of iron deficiency anemia (IDA) during pregnancy remains unclear. The study aimed to investigate the association between maternal weight indicators and IDA during pregnancy.
Methods: We conducted a cohort study to examine the association between maternal weight indicators, including prepregnancy body mass index and the rate of gestational weight gain (GWG), and the risk of IDA among Chinese pregnant women. Data about new-onset IDA at different trimesters from a national cross-sectional survey were collected; information regarding baseline variables and rate of GWG from women participating in the survey were retrospectively collected. Tested IDA and reported IDA were documented. Multilevel logistic regression to examine the association between maternal weight indicators and the risk of IDA after adjusting for potential confounders was conducted.
Results: This study enrolled 11,782 pregnant women from 24 hospitals from September 19, 2016, to November 20, 2016. Among those, 1515 (12.9%) IDA events were diagnosed through test (test IDA); 3915 (33.3%) were identified through test and patient reporting (composite IDA). After adjusting for confounders and cluster effect of hospitals, underweight pregnant women, compared with normal women, were associated with higher risk of test IDA (adjusted odds ratio [aOR]: 1.35, 95% confidence interval [CI]: 1.17–1.57 and composite IDA (aOR: 1.35, 95% CI: 1.21–1.51); on the contrary, overweight and obese women had lower risk of test IDA (aOR: 0.68, 95% CI: 0.54–0.86 overweight; aOR: 0.30, 95% CI: 0.13–0.69 obese) and composite IDA (aOR: 0.77, 95% CI: 0.67–0.90 overweight; aOR: 0.34, 95% CI: 0.21–0.55 obese). The higher rate of GWG was associated with higher risk of IDA (test aOR: 1.86 95% CI: 1.26–2.76; composite aOR: 1.54, 95% CI: 1.16–2.03).
Conclusions: Pregnant women who are underweight before pregnancy and who have faster GWG are more likely to develop IDA. Enforced weight control during pregnancy and use of iron supplements, particularly among underweight women, may be warranted.
结果：从2016年9月19日到2016年11月20日，我们从中国24家医院总共纳入11,782例孕妇。其中，1515 （12.9%）例发生经实验室诊断的缺铁性贫血，3915 （33.3%）发生复合缺铁性贫血（经实验室诊断和孕妇自报任意一种情况）。在调整了混杂和医院间的聚集效应后，我们发现：孕前低体重孕妇，相比于孕前体重正常孕妇，有更高的缺铁性贫血风险（实验室诊断缺铁性贫血：aOR:1.35, 95%CI :1.17-1.57 ；复合缺铁性贫血：aOR: 1.35, 95%CI :1.21-1.51）；相反，孕前超重和肥胖的孕妇有更低的经实验室诊断的缺铁性贫血风险（超重：aOR: 0.68, 95%CI: 0.54-0.86; 肥胖：aOR 0.30, 95%CI 0.13-0.69）和复合缺铁性贫血风险（超重：aOR: 0.77, 95%CI: 0.67-0.90; 肥胖：aOR: 0.34, 95%CI:0.21-0.55）。此外，更快的孕期体重增长速度也与缺铁性贫血的风险呈正相关（实验室诊断缺铁性贫血： aOR: 1.86, 95%CI: 1.26-2.76; 复合缺铁性贫血：aOR: 1.54, 95%CI :1.16-2.03）。
Keywords: Gestational Weight Gain; Iron Deficiency Anemia; Prepregnancy Body Mass Index
|How to cite this article:|
Tan J, Qi YN, He GL, Yang HM, Zhang GT, Zou K, Luo W, Sun X, Liu XH. Association between Maternal Weight Indicators and Iron Deficiency Anemia during Pregnancy: A Cohort Study. Chin Med J 2018;131:2566-74
|How to cite this URL:|
Tan J, Qi YN, He GL, Yang HM, Zhang GT, Zou K, Luo W, Sun X, Liu XH. Association between Maternal Weight Indicators and Iron Deficiency Anemia during Pregnancy: A Cohort Study. Chin Med J [serial online] 2018 [cited 2018 Nov 14];131:2566-74. Available from: http://www.cmj.org/text.asp?2018/131/21/2566/244109
| Introduction|| |
Anemia is a common comorbidity during pregnancy. Worldwide, the prevalence of pregnancy anemia was estimated 38% (95% confidence interval [CI]: 33–43%), meaning that 32 millions of pregnant women were affected. Of those, more than 50% was a result of iron deficiency (ID)., ID anemia (IDA) during pregnancy may cause a catastrophy of serious adverse outcomes, such as premature rupture of membrane, puerperal infection, fetal growth restriction, fetal hypoxia, and premature birth., Previous studies have identified several potential risk factors for IDA, such as poor nutritional status, multiple gestations, poor socioeconomic status, age over 30 years, multiparity, and shorter birth spacing.,
However, the potential impact of body weight on IDA has not been well investigated. Maternal weight indicators, including prepregnancy body mass index (BMI) and gestational weight gain (GWG), represent important measures of maternal metabolism and nutritional situation.,,, Their potential influences on the development of IDA are multifolds, and the directions of impacts seems inconsistent. On the one end, the demand for iron during pregnancy substantially increases due to expansion of maternal blood volume and rapid growth of placenta and fetus.,, Those women with higher BMI before pregnancy may have more iron stores for compensating the iron consumption during pregnancy and appear to less likely develop ID during pregnancy. On the other end, overweight, particularly obesity, is associated with underlying systemic inflammation, and the resulting elevation of hepcidin and serum ferritin,, may decrease iron absorption from dietary, and hemoglobin production., Thus, the overweight and obesity status may be associated with increased risk of IDA. In addition, faster weight gain may also suggest a higher likelihood of iron consumption during pregnancy. Consequently, all these potential biological rationales, which are inconsistent in the directions, could jointly affect iron consumption and production during pregnancy.
Up to now, only a few studies examined the association between maternal weight indicators and risk of IDA,,, and the findings were inconsistent. There also were important limitations among these published studies, such as the use of surrogate markers, (rather than the diagnosis of IDA) and restriction to special populations (e.g., obese pregnant women population, pregnant adolescents, or those at the third trimester). The paucity of reliable evidence has made the IDA management during pregnancy a topic of increasing interest. Therefore, we conducted a cohort study to specifically address the association between maternal weight indicators and risk of IDA.
| Methods|| |
The study was approved by the Research Ethics Committee of West China Second Hospital, Sichuan University (No. 2016-009) and registered at ClinicalTrials.gov (No. NCT02887963). All pregnant women will be informed about details of rights, benefits, and obligations of the study, and they sign informed consent if they agree to participate in the survey.
We used a cohort study design to examine the association between maternal weight indicators (i.e., prepregnancy BMI and GWG) and the risk of IDA among the Chinese pregnancy population. The data used for the analyses came from two sources: (1) a national cross-sectional study examining new-onset IDA at different gestation stages (i.e., first, second, and third trimesters) during pregnancy and (2) a survey accompanying with the cross-sectional study that used a predefined questionnaire to retrospectively collect information regarding baseline variables (e.g., prepregnancy BMI and other potential baseline confounders) and weight changes from baseline to the IDA measurement (i.e., at the time of cross-sectional test of IDA). [Figure 1] shows the conceptual framework of the cohort study.
|Figure 1: Conceptual framework of the cohort study. The information regarding IDA was collected during the cross-sectional study. The information regarding prepregnancy BMI (baseline), gestational weight gains, and other baseline characteristics (e.g., potential confounders) was retrospectively collected through the predefined questionnaire. The length of follow-up for patients at different gestational ages may differ; this difference was taken accounted of during the analysis. IDA: Iron deficiency anemia; BMI: Body mass index.|
Click here to view
Cross-sectional study and ascertainment of iron deficiency anemia
The cross-sectional study was conducted at 24 academic and nonacademic hospitals across China [Supplementary Table 1 [Additional file 1]]. We chose these hospitals using a multistage sampling method according to geographic regions and provinces (or municipal cities). Our study involved 24 hospitals located in 16 provinces or municipal cities across six regions in China (north, northeast, east, south, southwest, and northwest) according to the China National Bureau criteria. These 16 provinces or municipal cities cover about 750 million resident population (China Health Statistical Yearbook, 2012). We selected four hospitals from each region, including one tertiary teaching hospital and three other hospitals. The tertiary teaching hospital served as the coordinating site of that region.
The hospitals participating in the study can routinely test serum ferritin and hemoglobin at local laboratory using the qualified instrument and uniform method by experienced technicians and had a large number of patient visits. Clinician investigators at obstetric outpatient departments consecutively enrolled pregnant women receiving antenatal visits from September 19, 2016, to November 20, 2016.
Eligible pregnant women should meet all of the following inclusion criteria: (1) were receiving antenatal visit during the survey period, (2) agreed to conduct antenatal examination during antenatal visits, and (3) signed informed consent form. Those pregnant women were excluded, if they were participating in any clinical trial.
The IDA was identified through diagnosis at the defined antenatal visit or the latest visit occurred within 1 month (i.e., test IDA) or by patient reporting on pervious diagnosis (occurred more than 1 month). The outcome of our interest was the new-onset IDA, which included the test IDA and the composite of test IDA or patient reporting on previous diagnosis (i.e., composite IDA).
The test IDA was diagnosed with serum ferritin concentration <20 μg/L and hemoglobin <110 g/L at this antenatal visit or the latest antenatal visit (occurred within 1 month), according to the Chinese Guideline for Diagnosis and Treatment of IDA during Pregnancy. The ascertainment of previously diagnosed IDA was identified based on the self-reporting by pregnant women and ascertained by investigators using data recorded in the electronic medical records. We recorded the dates of all tests. All the methods and facilities of laboratory tests were checked for consistency by senior laboratory technicians.
We used a centralized, pilot-tested online electronic data capturing (EDC) system to collect, review, and manage data. The data were entered and stored at the EDC system. The EDC system had the capacity to edit (with tracked changes), store, and manage data. Once the investigators upload and confirm the data, they were not allowed to modify the data.
The statisticians at the Methods Center (Chinese Evidence- based Medicine Center) reviewed the uploaded data and conducted data verification. If missing or erroneous data were identified, the data queries were sent to the investigators for data checking and correction.
Accompanying survey to collect baseline data and weight changes
During the cross-sectional study, we used two predesigned and structured questionnaires to retrospectively collect baseline data and weight changes. The investigators collected information regarding anthropometry indicators, obstetric information from the electronic medical records (EMRs) of the 24 hospitals. All the data in 24 hospitals were further uploaded to EDC system.
The baseline data included demographic characteristics (age, nationality, local resident, education, occupation, marriage, urban or rural residence, and family population and income), diet pattern and nutrients supplements during gestation, history of diagnosis with IDA during this pregnancy, history of gestation, and lifestyle (e.g., active or passive smoking and drinking).
The data we extracted from EMR included anthropometry indicators (height, prepregnancy weight, and present weight at this antenatal visit), obstetric information (gestational week, history of pregnancy and parity, history of abortion and cesarean section, and use of assisted reproductive technology, etc.), medical comorbidities (hepatitis B, cardiovascular diseases, respiratory diseases, thyroid disease, diabetes mellitus [not including gestational diabetes], gynecological diseases, diseases of blood system, immune system, etc.), history of diagnosis with IDA during this pregnancy (timing of diagnosed IDA and treatment regimens), and pregnancy complications (hypertensive disorders, placenta previa, intrahepatic cholestasis, fetal growth restriction, fetal malformation, and stillbirth).
Among multiple gestational comorbidities, cardiovascular diseases included chronic hypertension, rheumatic heart disease, congenital heart disease, and myocardiosis. Gynecological diseases included uterine fibroids, ovarian cyst, cervicitis, polycystic ovarian syndrome, and ovarian adenomyosis. Diabetes included type 1 diabetes and type 2 diabetes. Thyroid diseases included hyperthyroidism, hypothyroidism, and subclinical hypothyroidism. Autoimmune diseases included systemic lupus erythematosus, antiphospholipid antibody syndrome, and Sjogren's disease. Digestive system disease included chronic gastritis, digestive ulcer, appendicitis, cholecystitis, chronic diarrhea, and hemorrhoids.
We examined two types of maternal weight indicators, including prepregnancy BMI and rate of GWG. The prepregnancy BMI was calculated through dividing prepregnancy weight (kilogram) by the square of height (meter) and was categorized according to the World Health Organization criteria (underweight: BMI <18.5 kg/m2; normal weight: BMI ≥18.5 kg/m2 and <25.0 kg/m2; overweight: BMI ≥25.0 kg/m2 and <30.0 kg/m2; obese: BMI ≥30.0 kg/m2). The GWG was calculated by maternal weight at antenatal visit and subtracting self-reported prepregnancy weight. The rate of GWG was calculated though GWG dividing by gestational week.
Sample size estimation
The current analysis was primarily based on the cross-sectional study, for which a single population proportion sample estimation formula was used to calculate the required minimum sample size (n), as follows:
According to our earlier retrospective database study involving 8 hospitals in Sichuan province, the prevalence of pregnant IDA was 3.96% (i.e., p ≈ 4%). Given d (precision of detecting change) = 0.004 (A tenth of the p), Zα/2 = 1.96 with 2-tailed α = 0.05, the estimated sample size is 9,220. Assuming the loss to follow-up is 5%, the expected sample size is 9706.
In discussing with the leading clinician investigators from the six regions, all agreed that the study could increase the sample size to 12,000, given the volume of participants and availability of research resources. At each region, the teaching hospital enrolled 800 participants, and each of the three other care facilities enrolled 400 participants.
We described the distribution of demographic characteristics, gestational characteristics, diet pattern, nutrients supplements, lifestyle, and gestational comorbidities according to prepregnancy BMI categories. We examined the difference of these characteristics among prepregnancy BMI categories using Pearson's Chi-square or Fisher's exact test for categorical variables, and ANONA analysis or rank-sum (Mann-Whitney) test for continuous variables.
We conducted univariable analyses of the associations between maternal weight indicators and the risk of new-onset IDA (test IDA and composite IDA). In order to adjust for the confounding effects, we conducted multivariable logistic regression analysis. Based on the biological rationale, previous research evidence, and statistical significance of univariable analyses (P < 0.1), we chose the following variables as covariates: maternal age (<35 years vs. ≥35 years), maternal race (Han vs. others), education (≥17 years vs. 13–16 years vs. 10–12 years vs. ≤9 years), local citizens (yes vs. no), area of residence (urban vs. rural), annual family income (<30,000 CNY vs. 30,000–79,999 CNY vs. 80,000–119,999 CNY vs. 120,000–199,999 CNY vs. ≥200,000 CNY), multiple gestations (yes and no), parity (<1 vs. ≥1), gestational week at the survey ( first vs. second vs. third trimester, defined as <13+6, 14–27+6 and ≥28 gestational week), egg intake per week (unit of 0.5 kg), meat intake per week (unit of 0.5 kg), smoking before pregnancy (yes vs. no), nausea and/or vomiting during pregnancy (no vs. slight vs. severe), multivitamins supplement (yes vs. no), calcium supplement (yes vs. no), and multiple gestational comorbidities (yes vs. no).
To explore whether the effects of prepregnancy BMI categories on risk of IDA varied among different trimesters, we included an interaction between prepregnancy BMI groups and gestational week at the survey ( first, second, and third trimesters) in multivariable logistic regression analysis.
Given the potential cluster effect of care organization across regions, we additionally developed multilevel logistic regression models to assess the association between maternal weight indicators and risk of IDA, on the basis of the above multiple regression models. In these multilevel logistic regression models, the effects of different hospitals were deemed random. We reported the adjusted odds ratio (aOR), 95% confidence interval (CI), and the responding P value for all models. In light of limited exposure for pregnant women in the first trimester, we conducted a sensitivity analysis by excluding pregnant women at the first trimester.
We checked for missing data across all variables and included the patients with complete data in our analyses. We used STATA 13.0 (StataCorp College Station, Texas, USA) for the statistical analysis.
| Results|| |
We enrolled 12,403 pregnant women receiving antenatal visits from September 19, 2016, to November 20, 2016, among 24 hospitals. By excluding 621 pregnant women with preexisted hematological diseases, 11,782 pregnant women were eligible. We checked for missing data among all reported variables. Finally, we included 11,757 and 11,759 pregnant women for the analyses on test IDA and composite IDA [Figure 2].
|Figure 2: Flowchart of included population. IDA: Iron deficiency anemia; GWG: Gestational weight gain.|
Click here to view
The median maternal age was 29 years (interquartile range 27–33). Among the included women, 987 (8.4%) were at the first trimester, 3365 (28.6%) at the second trimester, and 7430 (63.1%) at the third trimester; 2028 (17.2%) were underweight, 8476 (72.0%) normal, 1133 (9.6%) overweight, and 145 (1.2%) obese. In total, we identified 1515 (12.9%) test IDA events and 3915 (33.3%) composite IDA events.
[Table 1] reported baseline characteristics of pregnant women according to prepregnancy BMI categories. There were statistically significant differences in region, maternal age, race, education, family income, smoking, parity, meat intake, egg intake, gynecological diseases, diabetes, thyroid diseases, digestive system diseases, and calcium across the prepregnancy items.
By univariable analyses, we found that underweight pregnant women had higher risk of new-onset IDA than those with normal weight. In contrast, both overweight and obese women had lower risk of IDA. The analysis of rate of GWG suggested that more weight gain was associated with higher risk of IDA [Table 2].
|Table 2: Univariable analyses of associations between maternal weight indicators and risk of IDA|
Click here to view
The multivariable regression analysis showed that, compared with normal weight, underweight women were associated with higher risk of IDA (test IDA: aOR: 1.33, 95% CI: 1.15–1.53; composite IDA: 1.31, 1.17–1.45), and overweight and obese women had lower risk of IDA (overweight: test IDA: 0.65, 0.52–0.81; composite IDA: 0.80, 0.69–0.92; obese women: 0.27, 0.12–0.63; and 0.36, 0.22–0.57). The higher rate of GWG was associated with higher risk of IDA (test IDA: 1.76, 1.21–2.57; composite IDA: 1.55, 1.16–2.03). The additional analysis by including interaction between prepregnancy BMI categories and trimesters in the multivariable regression analysis was not statistically significant (interaction P = 0.981 for test IDA, P = 0.495 for composite IDA), suggesting that the effect of pregnancy BMI on the risk of IDA was consistent across the trimesters and that the analysis by including all women at the three trimesters was plausible. To explore the cluster effect of 24 hospitals, we test the random-effects parameters. The results suggested that there was substantial cluster effect among 24 hospitals (estimate 0.61, standard error 0.19, P < 0.001). Therefore, we further conducted multilevel regression analysis to adjust the cluster effect of hospitals. We found that, compared with normal weight, underweight women increased the risk of IDA (test IDA: aOR, 1.35, 95% CI: 1.17–1.57; composite IDA: 1.35, 1.21–1.51), and overweight and obese women had lower risk of IDA (overweight: test IDA: 0.68, 0.54–0.86; composite IDA: 0.77, 0.67–0.90; obese women: 0.30, 0.13–0.69; and 0.34, 0.21–0.55). The higher rate of GWG was associated with higher risk of IDA (test IDA: 1.86, 1.26–2.76; composite IDA: 1.54, 1.16–2.03) [Table 3]. The sensitivity analyses by excluding pregnant women at the first trimester showed similar findings, suggesting robustness of the results [Table 4].
|Table 3: Multivariable logistic regression and multilevel regression analyses|
Click here to view
|Table 4: Sensitivity analyses by excluding pregnant women at the first trimester|
Click here to view
| Discussion|| |
Main findings and implications
In our study, we found that women with lower BMI before pregnancy were more likely to develop IDA during pregnancy, and that faster GWG was also associated with higher risk of IDA. The results were similar, regardless of the use of different outcomes measures (i.e., test IDA and composite IDA).
One may argue that our study enrolled pregnant women at different trimesters, and the magnitude of association between prepregnancy BMI and the risk of IDA may differ across different trimesters (i.e., presence of interaction). However, our analysis of the interaction effect was not statistically significant, suggesting that the effects of prepregnancy BMI on the risk of IDA were consistent across the whole gestation period. The sensitivity analysis by excluding women at the first trimester supports the results from the main analyses.
Our study findings are important and timely to clarify the roles of maternal weight indicators on the development of IDA. In particular, the conflicting biological rationales, stated earlier, have made the clinical practice challenging and evidence-based recommendations are not readily available.,, In summary, our findings may have three important implications for weight management and the use of iron supplements. First, our findings clearly emphasize, on top of other studies investigating adverse effects of excessive weight gain, that fast weight gain during pregnancy is undesirable. Weight control has to be reinforced among pregnant women. Second, those women who are underweight pregnancy are probably high-risk population for IDA, and prophylactic use of iron supplements may be warranted. Third, the development of IDA could occur at any gestational age ( first to third trimester), regardless of BMI before pregnancy. Early use of oral supplements might be desirable, particularly those who are underweight. Meanwhile, because the study is observational in nature, one should use the finding with cautions. In the test of the hypotheses that early use of iron supplements and weight control reinforcement could reduce the risk of IDA, randomized trials continue to serve the best source of evidence.
Comparison with previous studies
Several studies examined the association between BMI and iron status, in pregnant population and other populations. The findings were inconsistent.,,, In pregnant adolescents' population, BMI was found to be inversely associated with surrogate markers of ID, such as hemoglobin, serum ferritin, and transferrin receptor. In another study, obese women were less likely to have low serum ferritin or the risk of ID., These findings came from cross-sectional analyses and were limited for making causal inference. In the third study, maternal obesity during pregnancy was found to be inversely associated with ID, which was however measured by transferrin receptor and body iron. The latest study, involving only 255 women, found no association between maternal weight indicators and ID.
In comparison, our study, including a large number of participants, clearly suggested that maternal weight indicators were statistically associated with the risk of IDA, a more clinically important outcome measure. We used the cohort study design to explore the association, by which a causal inference is possible. Given the large number of events, we were able to adjust for the confounding effects. We also were able to investigate the impacts of both prepregnancy BMI and rate of GWG on the IDA risk. In addition, we have examined the underweight population in association with the IDA risk. All these analyses were not explored in the previous studies. Therefore, our findings provided more comprehensive and clinically meaningful findings.
Strengths and limitations
Our study has several strengths. To the best of knowledge, this is the largest study that investigates the impact of maternal weights indicators on the risk of IDA. We used rigorous methods for data collection and data management. Second, we prespecified and adjusted for a number of potentially important confounders, given the large number of events we collected. Third, we have conducted through analyses by examining the consistency of findings using two relevant outcomes (i.e., test IDA and composite IDA). Fourth, we examined for the putative interaction between prepregnancy BMI categories and different trimesters and suggested the effects of prepregnancy BMI categories on IDA among different trimesters were not varied. We additionally used multilevel regression model to control for the cluster effect among different hospitals. We also conducted a sensitivity analysis to check for robustness of findings.
Our study also has a few limitations. First, we used the local laboratory facilities to examine the concentrations of serum ferritin and hemoglobin. We realized that the variable performance of laboratory facilities may occur. This decision was made primarily because of the study feasibility, limited availability of resources, and efficacy of the study. Nevertheless, we contacted all the 24 hospitals to ensure that their facilities were well calibrated and required that the key parameters were provided. Second, the data for the cohort study primarily came from a cross-sectional study and the accompanying survey that collected prepregnancy baseline data. In nature, the data on the exposure and confounders were collected retrospectively. There was a risk of bias associated with retrospective data collection (e.g., more misclassifications and inaccuracies).
In conclusion, our study suggested that lower prepregnancy BMI was associated with higher risk of IDA, and pregnant women with faster GWG may be more likely to develop IDA. These findings apply to pregnant women at all trimesters. Given the potential harms of IDA on pregnant women and prenatal infants, prophylactic iron supplement should be administered for high risk population of IDA.
Supplementary information is linked to the online version of the paper on the Chinese Medical Journal website.
Financial support and sponsorship
This study was supported by grants from the National Natural Science Foundation of China (No. 71704122), the National Key Research and Development Program of Reproductive Health and Major Birth Defects Control and Prevention (No. 2016YFC1000406), and “Thousand Youth Talents Plan” of China (No. D1024002).
Conflicts of interest
There are no conflicts of interest.
| References|| |
Stevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR, Branca F, et al.
Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995-2011: A systematic analysis of population-representative data. Lancet Glob Health 2013;1:e16-25. doi: 10.1016/S2214-109X(13)70001-9.
Haider BA, Olofin I, Wang M, Spiegelman D, Ezzati M, Fawzi WW, et al.
Anaemia, prenatal iron use, and risk of adverse pregnancy outcomes: Systematic review and meta-analysis. BMJ 2013;346:f3443. doi: 10.1136/bmj.f3443.
Medicine Chinese Society of Perinatal. Guideline for diagnosis and treatment of iron deficiency and iron deficiency anemia in pregnancy. Chin J Perinat Med 2014;17:451-4. doi: 10.3760/cma.j.issn.1007-9408.2014.07.006.
Breymann C. Iron deficiency anemia in pregnancy. Semin Hematol 2015;52:339-47. doi:/10.1053/j.seminhematol.2015.07.003
Rezk M, Marawan H, Dawood R, Masood A, Abo-Elnasr M. Prevalence and risk factors of iron-deficiency anaemia among pregnant women in rural districts of Menoufia Governorate, Egypt. J Obstet Gynaecol 2015;35:663-6. doi: 10.3109/01443615.2014.991289.
Shafique S, Akhter N, Stallkamp G, de Pee S, Panagides D, Bloem MW, et al.
Trends of under – And overweight among rural and urban poor women indicate the double burden of malnutrition in Bangladesh. Int J Epidemiol 2007;36:449-57. doi: /10.1093/ije/dyl306.
Mamun AA, Callaway LK, O'Callaghan MJ, Williams GM, Najman JM, Alati R, et al.
Associations of maternal pre-pregnancy obesity and excess pregnancy weight gains with adverse pregnancy outcomes and length of hospital stay. BMC Pregnancy Childbirth 2011;11:62. doi: 10.1186/1471-2393-11-62.
Kim SY, Sharma AJ, Sappenfield W, Wilson HG, Salihu HM. Association of maternal body mass index, excessive weight gain, and gestational diabetes mellitus with large-for-gestational-age births. Obstet Gynecol 2014;123:737-44. doi: 10.1097/AOG.0000000000000177.
Goldstein RF, Abell SK, Ranasinha S, Misso M, Boyle JA, Black MH, et al.
Association of gestational weight gain with maternal and infant outcomes: A Systematic review and meta-analysis. JAMA 2017;317:2207-25. doi: 10.1001/jama.2017.3635.
Welke L, Koenig MD, Thomson JL, Nemeth E, White-Traut R, McFarlin BL, et al.
Iron metabolism in African American women in the second and third trimesters of high-risk pregnancies. J Obstet Gynecol Neonatal Nurs 2017;46:148-58. doi: 10.1016/j.jogn.2016.06.013.
Di Renzo GC, Spano F, Giardina I, Brillo E, Clerici G, Roura LC, et al.
Iron deficiency anemia in pregnancy. Womens Health (Lond) 2015;11:891-900. doi: 10.2217/whe.15.35.
Wendt AS, Jefferds ME, Perrine CG, Halleslevens P, Sullivan KM. Obese women less likely to have low serum ferritin, Nicaragua. Public Health Nutr 2015;18:736-41.doi: 10.1017/S1368980014000755.
Ausk KJ, Ioannou GN. Is obesity associated with anemia of chronic disease? A population-based study. Obesity (Silver Spring) 2008;16:2356-61. doi: 10.1038/oby.2008.353.
Sal E, Yenicesu I, Celik N, Pasaoglu H, Celik B, Pasaoglu OT, et al
. Relationship between obesity and iron deficiency anemia: Is there a role of hepcidin? Hematology (Amsterdam, Netherlands) 2018;23:542-8. doi: 10.1080/10245332.2018.1423671.
Tussing-Humphreys L, Pusatcioglu C, Nemeth E, Braunschweig C. Rethinking iron regulation and assessment in iron deficiency, anemia of chronic disease, and obesity: Introducing hepcidin. J Acad Nutr Diet 2012;112:391-400. doi: 10.1016/j.jada.2011.08.038.
Dao MC, Sen S, Iyer C, Klebenov D, Meydani SN. Obesity during pregnancy and fetal iron status: Is hepcidin the link? J Perinatol 2013;33:177-81. doi: 10.1038/jp.2012.81.
Young MF, Griffin I, Pressman E, McIntyre AW, Cooper E, McNanley T, et al.
Utilization of iron from an animal-based iron source is greater than that of ferrous sulfate in pregnant and nonpregnant women. J Nutr 2010;140:2162-6. doi: 10.3945/jn.110.127209.
Finch CA, Bellotti V, Stray S, Lipschitz DA, Cook JD, Pippard MJ, et al
. Plasma ferritin determination as a diagnostic tool. West J Med 1986;145:657-63.
Feelders RA, Vreugdenhil G, Eggermont AM, Kuiper-Kramer PA, van Eijk HG, Swaak AJ, et al.
Regulation of iron metabolism in the acute-phase response: Interferon gamma and tumour necrosis factor alpha induce hypoferraemia, ferritin production and a decrease in circulating transferrin receptors in cancer patients. Eur J Clin Invest 1998;28:520-7. doi:/10.1046/j.1365-2362.1998.00323.x.
Jones AD, Zhao G, Jiang YP, Zhou M, Xu G, Kaciroti N, et al.
Maternal obesity during pregnancy is negatively associated with maternal and neonatal iron status. Eur J Clin Nutr 2016;70:918-24. doi: 10.1038/ejcn.2015.229.
Cao C, Pressman EK, Cooper EM, Guillet R, Westerman M, O'Brien KO, et al.
Prepregnancy body mass index and gestational weight gain have no negative impact on maternal or neonatal iron status. Reprod Sci 2016;23:613-22. doi: 10.1177/1933719115607976.
Tan J, Liu XH, Yu C, Chen M, Chen XF, Sun X, et al.
Effects of medical co-morbidities on severe maternal morbidities in china: A multicenter clinic register study. Acta Obstet Gynecol Scand 2015;94:861-8. doi: 10.1111/aogs.12657.
Calje E, Skinner J. The challenge of defining and treating anemia and iron deficiency in pregnancy: A study of new Zealand midwives' management of iron status in pregnancy and the postpartum period. Birth 2017;44:181-90. doi: 10.1111/birt.12282.
Zimmermann MB, Zeder C, Muthayya S, Winichagoon P, Chaouki N, Aeberli I, et al.
Adiposity in women and children from transition countries predicts decreased iron absorption, iron deficiency and a reduced response to iron fortification. Int J Obes (Lond) 2008;32:1098-104. doi: /10.1038/ijo.2008.43.
Kordas K, Fonseca Centeno ZY, Pachón H, Jimenez Soto AZ. Being overweight or obese is associated with lower prevalence of anemia among Colombian women of reproductive age. J Nutr 2013;143:175-81. doi: 10.3945/jn.112.167767.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]