Overweight/obesity (OWT/OB) and anaemia have globally emerged as major nutritional problems in low- and middle-income countries. The prevalence of overweight/obesity is 34 %, which has doubled in the past three decades to 1·5 billion overweight/obese adults worldwide. The prevalence of overweight/obesity is higher in women than men(Reference Seidell and Halberstadt1). An alarming trend has also been observed in the Southeast Asia region, where overweight/obesity escalated from 18·9 % in 2006 to 26·6 % in 2016(Reference Tham, Abdul Ghani and Cua2–4). Simultaneously, the latest data in 2019 reported that anaemia appears to afflict half a billion women of reproductive age (WRA, 15–49 years), affecting 32 million pregnant women and 539 million non-pregnant women. Southeast Asia is one of the most affected regions, with an estimated 244 million women being affected(5). Anaemia in WRA causes fatigue and lower productivity, and more importantly, it impacts their nutritional status during pregnancy(Reference Mawani, Ali and Bano6,Reference Nguyen, Gonzalez-Casanova and Nguyen7) . Anaemia during pregnancy is linked to adverse maternal and fetal health consequences, including premature labour, maternal mortality, low birth weight, a weakened immune system resulting in a higher burden of infectious diseases, slowed fetal and child growth and development, and neonatal anaemia(Reference Viteri8–Reference García, Long and Rosado11).
In most low- and middle-income countries, more than half of anaemia cases are iron deficiency anaemia (IDA), which is a consequence of iron deficiency (ID)(12,Reference Turawa, Awotiwon and Dhansay13) . Additionally, the double burden of malnutrition in terms of the co-existence of overweight/obesity and anaemia is a significant public health problem in low- and middle-income countries(Reference Wells, Sawaya and Wibaek14,Reference Popkin, Corvalan and Grummer-Strawn15) . In susceptible population groups such as women, overweight/obesity and IDA may have a more negative impact on health than each of these disorders alone(Reference Irache, Anjorin and Caleyachetty16). A study conducted with nationally representative data from fifty-two low- and middle-income countries in 2022 reported a prevalence of concurrent overweight/obesity and anaemia among 12·4 % of WRA(Reference Irache, Gill and Caleyachetty17) Studies have reported that ID may be precipitated by overweight/obesity in non-pregnant WRA(Reference Herter-Aeberli, Thankachan and Bose18) as well as pregnant ones(Reference Ahmed19). This may be attributed to adiposity-induced low-grade inflammation due to cytokine production, which increases hepcidin synthesis and leads to a lower Fe absorption rate and lower Fe bioavailability(Reference Wawer, Hodyl and Fairweather-Tait20–Reference Tan, Qi and He22). Thus, overweight/obese individuals are more likely to have IDA compared to individuals with normal weight. However, current literature has reported contrasting results; some studies revealed that OWT/OB was associated with ID, lower serum ferritin and lower serum Fe(Reference Abbas, Adam and Rayis23–Reference Cheng, Bryant and Rooney25), while others did not find any association(Reference Jordaan, Van den Berg and Van Rooyen26–Reference Kordas, Centeno and Pachón28). Hence, there is a need to explore the existing evidence systematically to identify the association between overweight/obesity and IDA.
Only one systematic review published in 2011 examined the relationship between overweight/obesity and Fe status. It reported a reduced TS% but increased Hb and ferritin concentrations(Reference Cheng, Bryant and Cook29) in overweight/obese individuals, which is contrary to the existing evidence regarding overweight/obesity and IDA relationship(Reference Cepeda-Lopez, Aeberli and Zimmermann30). However, the review included studies involving a wide range of population groups, including WRA, postmenopausal women, male and bariatric surgery patients. It is well known that the physiological differences between various population groups can influence the Fe biomarker levels and, thus, the reported association. Further, they failed to adjust serum ferritin concentration for inflammation and explain the hepcidin-induced inflammation among obese individuals due to a few studies that included hepcidin and inflammation markers. Filling these gaps is crucial for future studies in concluding the association between overweight/obesity and IDA, which will allow early prevention of IDA in this population. Thus, the present systematic review examined the current evidence on how overweight/obesity impacts Fe status among the specific population group of WRA.
Methods
Study selection
Studies that reported an association between overweight/obesity and Fe status or anaemia in pregnant and/or non-pregnant WRA were included in this systematic review. With the inclusion criteria applied, this systematic review only included studies involving pregnant women and/or non-pregnant WRA (18–50 years), reporting at least one Fe status marker (Hb, serum Fe, serum ferritin, transferrin saturation (TS%) and soluble transferrin receptor (sTfR)), with or without hepcidin or inflammation marker (C-reactive protein (CRP), alpha(1)-acid glycoprotein (AGP), IL-6 and leptin), using BMI as the criteria to define overweight/obesity, and being published in English with available full texts. In detail, BMI cut-offs are used to classify nutritional status, which is defined using WHO classification, either global or Asian classification, depending on the study population. Anaemia was defined using WHO cut-offs for pregnant (Hb <110 g/l) and non-pregnant women (Hb <120 g/l)(12). Furthermore, we refrained from predefining any specific cut-off points for ID due to variations observed in different studies.
However, this systematic review did not include studies involving obese patients who had undergone bariatric surgery and participants with co-morbidities (diabetes, metabolic syndrome, hypertension and liver diseases), non-original articles (literature/systematic reviews, meta-analyses, comments, short communications and editorial letters), being unpublished and being accessible for abstract only.
Search strategy
A literature search was performed in the following electronic databases: SCOPUS (Griffith University Library; https://www-scopus-com.libraryproxy.griffith.edu.au/), Medline (Ovid; https://ovidsp-dc1-ovid-com.libraryproxy.griffith.edu.au/), CINAHL (EBSCO host; https://web-p-ebscohost-com.libraryproxy.griffith.edu.au/ehost/search), Embase (https://www-embase-com.libraryproxy.griffith.edu.au/search/) and Web of Science (https://www.webofscience.com/wos/woscc/basic-search). The following search terms used were (anaemia OR anaemia OR iron-deficient* OR iron status OR nutritional biomarker) and (obes* OR overweight OR BMI OR nutritional status), and (women OR women of reproductive age OR women in reproductive age OR female OR pregnant women) (see online supplementary material, Supplementary 1). To identify potential publications, we restricted our search to articles written in English, but there was no restriction on the research type. Figure 1 shows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram of the literature search(Reference Page, McKenzie and Bossuyt31).
Fig. 1 PRISMA diagram of the literature search process. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; WRA, women of reproductive age.
Data extraction
The next step was title/abstract screening, where we included papers meeting the eligibility criteria. This was followed by full-text screening by two independent researchers (QR and PM). Any discrepancies arising at this final screening stage were resolved through consensus building. The researchers engaged in thorough discussions to identify the root causes of discrepancies, allowed for exploring different perspectives and facilitated the alignment of interpretations. In cases where consensus could not be reached immediately, a third reviewer (FA) with expertise in the field was consulted to provide additional insights and facilitate resolution.
Data extracted from each study consisted of journal identity (title, first author, year and country of study), study methodology (study design, sample size and calculation, exposure and outcome measurements, and statistical analysis), participant characteristics (age, pregnancy status, BMI and other overweight/obesity diagnoses), and means and standard deviations (or medians and interquartile ranges) of haematological markers (Hb, MCV, MCH, MCHC and erythrocytes count), Fe markers (serum ferritin, serum Fe, serum hepcidin, TS%, sTfR and total iron-binding capacity) or inflammatory markers (CRP, IL-6, leptin and AGP) if available. In comparison to cross-sectional studies, we only extracted baseline data from the longitudinal cohort and interventional studies.
Quality assessment of the studies
A critical appraisal of the quality of the selected research papers was performed using the Joanna Briggs Institute’s (JBI) critical appraisal tools for quantitative research(Reference Barker, Stone and Sears32) and an additional quality checklist for epidemiological studies previously used in another systematic review(Reference Cheng, Bryant and Cook29). Considering the variation of study design of included studies in this systematic review, JBI was appropriate to use as it covers the assessment of studies with a wide range of study designs and is considerably the newest validated tool for quality assessment(Reference Ma, Wang and Yang33). The following screening questions used to check for quality: were was the study population sufficiently described?; were the sampling frame and technique sufficiently described?; how was the sample size determined?; were the participants’ inclusion and exclusion criteria appropriate?; were exposures and outcomes (anthropometric and haematological) measured using appropriate methods?; were important confounders accounted for by exclusion, data separation or statistical adjustment?; was the statistical analysis sufficiently described? For the case–control studies, an additional criterion ‘Were case and control groups correctly defined?’ was employed. Each paper was assigned one score if it met the criteria described above. The maximum score based on the quality assessment was 6. See online supplementary material, Supplementary Tables 1 (for pregnant women) and 2 (for non-pregnant women) present the final quality assessments.
Results
Studies included in the review
Figure 1 presents a PRISMA diagram summarising the literature search process. We used a combination of keywords to achieve a more robust search (see online supplementary material, Supplemental Fig. S1). Our initial search of four electronic databases yielded 14 476 articles involving pregnant and non-pregnant women. After removing duplicates using automation tools (Covidence; https://covidence.org/), 9870 papers remained for title and/or abstract screening. After screening the full texts by applying the inclusion and exclusion criteria, twelve original articles addressing pregnant women and fifteen original articles involving non-pregnant women were included in the analysis.
Characteristics of the included studies
Table 1 shows the characteristics of the studies included in this systematic review. Among studies involving pregnant women, there were two cross-sectional(Reference Abbas, Adam and Rayis23,Reference Shin, Lee and Song40) , two case–control(Reference Dao, Sen and Iyer34,Reference Rahma, Lumbanraja and Lubis39) , seven prospective cohorts(Reference Scholing, Olthof and Jonker21,Reference Mayasari, Hu and Chao24,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35–Reference Mocking, Savitri and Uiterwaal38,Reference Garcia-Valdes, Campoy and Hayes41) and one retrospective cohort study(Reference Tan, Qi and He22). Of the twelve studies, three articles recruited participants in their early pregnancy period (n 3; 25 %)(Reference Scholing, Olthof and Jonker21,Reference Abbas, Adam and Rayis23,Reference Mocking, Savitri and Uiterwaal38) , four articles examined participants in the second or third trimester(Reference Dao, Sen and Iyer34,Reference Jones, Zhao and Jiang36,Reference Rahma, Lumbanraja and Lubis39,Reference Garcia-Valdes, Campoy and Hayes41) , four articles examined the participants throughout the pregnancy(Reference Mayasari, Hu and Chao24,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Liabsuetrakul37,Reference Shin, Lee and Song40) and one article recruited participants at the onset of IDA at any time point during the pregnancy(Reference Tan, Qi and He22). Pre-pregnancy BMI was used to measure overweight/obesity in pregnant women, except in Abbas et al.’s study, which used early-pregnancy BMI(Reference Abbas, Adam and Rayis23). Other three studies also reported gestational weight gain(Reference Tan, Qi and He22,Reference Jones, Zhao and Jiang36,Reference Garcia-Valdes, Campoy and Hayes41) as an additional weight indicator during pregnancy. In terms of measured outcomes, each study reported different combinations of markers. To measure Fe status in pregnant women population, concentrations of ferritin were most frequently reported (n 8, 67 %)(Reference Scholing, Olthof and Jonker21,Reference Abbas, Adam and Rayis23,Reference Mayasari, Hu and Chao24,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Jones, Zhao and Jiang36,Reference Rahma, Lumbanraja and Lubis39–Reference Garcia-Valdes, Campoy and Hayes41) , followed by Hb (n 6, 50·0 %)(Reference Abbas, Adam and Rayis23,Reference Mayasari, Hu and Chao24,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35–Reference Mocking, Savitri and Uiterwaal38) and serum Fe (n 5, 41·7 %)(Reference Mayasari, Hu and Chao24,Reference Dao, Sen and Iyer34,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Shin, Lee and Song40,Reference Garcia-Valdes, Campoy and Hayes41) . Serum transferrin receptor (sTfR) was reported in three studies along with Hb and ferritin(Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Jones, Zhao and Jiang36,Reference Garcia-Valdes, Campoy and Hayes41) . Few studies reported TS% (n 3)(Reference Mayasari, Hu and Chao24,Reference Dao, Sen and Iyer34,Reference Garcia-Valdes, Campoy and Hayes41) . In terms of inflammatory markers, CRP level was the most commonly used marker, which was employed in three studies(Reference Dao, Sen and Iyer34–Reference Jones, Zhao and Jiang36), followed by IL-6 in two studies(Reference Dao, Sen and Iyer34,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35) and leptin in one study(Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35). Flores-Quijano et al. measured IL-6 and leptin apart from CRP levels(Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35). Hepcidin was only measured by five out of twelve reviewed studies(Reference Mayasari, Hu and Chao24,Reference Dao, Sen and Iyer34,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Rahma, Lumbanraja and Lubis39,Reference Garcia-Valdes, Campoy and Hayes41) . Almost all studies (n 11, 92 %) compared the overweight/obesity group with the non-overweight/obesity group in the analysis(Reference Scholing, Olthof and Jonker21–Reference Mayasari, Hu and Chao24,Reference Dao, Sen and Iyer34–Reference Liabsuetrakul37,Reference Rahma, Lumbanraja and Lubis39–Reference Garcia-Valdes, Campoy and Hayes41) ; only one study did not report a non-obese control group and rather categorised the BMI into tertiles(Reference Mocking, Savitri and Uiterwaal38). Based on the methodological quality assessment, the overall study quality was moderate. Most studies (n 10) met at least four out of six quality indicators (see online supplementary material, Supplementary Table 1). Scores ranged from four to six, with a mean score of 4·7. One study that did not sufficiently describe the statistical analysis as a quality assessment indicator was excluded from the analysis.
Table 1 Characteristics of the included studies
AGP, alpha(1)-acid glycoprotein; OWT, overweight; OB, obesity/obese; NW, normal weight; CRP, C-reactive protein; Lp, leptin; MCH, mean corpuscular haemoglobin; MCV, mean corpuscular volume; MCHC, mean corpuscular haemoglobin concentration; sTfR, soluble transferrin receptor; SF, serum ferritin; TS%, transferrin saturation; TIBC, total iron-binding capacity; IDA, iron deficiency anaemia; HH, household; DHS, Demographic Health Survey; MUAC, mid-upper arm circumference; NPNL, non-pregnant non-lactating; VLED, very low energy diet; DMPA, depot medroxyprogesterone acetate; IUD, intra-uterine device; OCP, oral contraceptive pill.
We also found that four studies (33 %) did not consider any confounders, such as maternal characteristics, dietary intake, supplement use or the presence of infectious diseases, in their analysis, which might have affected the reported results(Reference Abbas, Adam and Rayis23,Reference Dao, Sen and Iyer34,Reference Rahma, Lumbanraja and Lubis39,Reference Garcia-Valdes, Campoy and Hayes41) . In the studies that considered the effect of confounding variables in the statistical analysis, the most frequently considered confounders were maternal age, gestational age and socio-economic status(Reference Scholing, Olthof and Jonker21,Reference Tan, Qi and He22,Reference Mayasari, Hu and Chao24,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35–Reference Mocking, Savitri and Uiterwaal38,Reference Shin, Lee and Song40) . Tan et al.’s study reported the most comprehensive list of identified confounders, including maternal age, race, education, local citizens, area of residence, annual family income, multiple gestations, parity, gestational age at the time of the survey, egg intake per week, meat intake per week, smoking before pregnancy, nausea and/or vomiting during pregnancy, multivitamin supplementation, Ca supplementation and multiple gestational co-morbidities(Reference Tan, Qi and He22). However, their study failed to include Fe intake, birth spacing, infections and inflammatory disorders. Despite Fe supplementation being a well-known confounder, three of the twelve reviewed studies controlled this variable(Reference Tan, Qi and He22,Reference Mayasari, Hu and Chao24,Reference Jones, Zhao and Jiang36) .
In non-pregnant populations, fifteen studies were included(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25–Reference Kordas, Centeno and Pachón28,Reference Cepeda-Lopez, Aeberli and Zimmermann30,Reference Rad, Sefidgar and Tamadoni42–Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50) . The majority of the studies used cross-sectional designs (n 13, 86·7 %)(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25,Reference Jordaan, Van den Berg and Van Rooyen26,Reference Kordas, Centeno and Pachón28,Reference Rad, Sefidgar and Tamadoni42,Reference Chang, Chen and Owaga44–Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) . Among the remaining two studies, one was an interventional study (43) and the other was a cohort study(Reference Karl, Lieberman and Cable27). In terms of Fe markers, Hb level was the most frequently reported marker used in non-pregnant women (n 12; 80 %)(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25–Reference Kordas, Centeno and Pachón28,Reference Rad, Sefidgar and Tamadoni42–Reference Chang, Chen and Owaga44,Reference Fricker, Le Moel and Apfelbaum46,Reference Hiremath, Kumar and Huchchannavar47,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) , followed by serum ferritin (n 9, 60·0 %)(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25–Reference Kordas, Centeno and Pachón28,Reference Beard, Borel and Peterson43,Reference Chang, Chen and Owaga44,Reference Fricker, Le Moel and Apfelbaum46,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50) , TS% (n 6, 40 %)(Reference Cheng, Bryant and Rooney25–Reference Karl, Lieberman and Cable27,Reference Beard, Borel and Peterson43,Reference Fricker, Le Moel and Apfelbaum46,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) , serum/plasma Fe (n 5, 33·3 %)(Reference Cheng, Bryant and Rooney25,Reference Beard, Borel and Peterson43,Reference Chang, Chen and Owaga44,Reference Fricker, Le Moel and Apfelbaum46,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) and sTfR (n 4, 26·7 %)(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25,Reference Karl, Lieberman and Cable27,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50) . For inflammatory markers, most studies used CRP level (n 6, 40 %)(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25,Reference Jordaan, Van den Berg and Van Rooyen26,Reference Kordas, Centeno and Pachón28,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) , followed by AGP (n 1, 6·7 %)(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50) and TNF-α (TNF-α) (n 1, 6·7 %)(Reference Karl, Lieberman and Cable27). Hepcidin level was only measured by two out of fifteen studies (13·3 %)(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25) . Among the studies conducted on non-pregnant women, only 73 % of the studies met at least four quality assessment indicators. In addition, 40 % of the studies did not consider the effects of any potential confounders in their statistical analyses, which might have introduced significant bias while concluding the findings(Reference Herter-Aeberli, Thankachan and Bose18,Reference Garcia-Valdes, Campoy and Hayes41,Reference Rad, Sefidgar and Tamadoni42,Reference Chang, Chen and Owaga44,Reference Hiremath, Kumar and Huchchannavar47,Reference Hisa, Haruna and Hikita48) . Studies conducted by Fricker et al. (Reference Fricker, Le Moel and Apfelbaum46) and Nainggolan et al. (Reference Nainggolan, Hapsari and Titaley49) measured sex, menopausal status, oral contraceptive use, blood donation, Fe treatment/drugs, amenorrhoea, chronic disease, education level, physical activity, consumption of fruits and vegetables, and the presence of infectious or non-communicable diseases as confounders; however, both failed to account for dietary intake (Fe intake, Fe enhancers and inhibitors) and supplement use. Overall, quality scores ranged from 2 to 6, with a mean score of 4·1. Studies with a quality score of less than 2 usually did not report outcome measurements, statistical analysis or sampling procedures, and those studies were excluded from the analysis (see online supplementary material, Supplementary Table 2).
Outcomes in pregnant women participants
Summaries of the extracted data for the pregnant women are presented in see online supplementary material, Supplementary Table 3. The prevalence of overweight/obesity among pregnant women in this review ranged from 10·8 % to 69·8 %(Reference Scholing, Olthof and Jonker21–Reference Mayasari, Hu and Chao24,Reference Jones, Zhao and Jiang36,Reference Liabsuetrakul37,Reference Shin, Lee and Song40) . In comparison, anaemia prevalence ranged from 12·7 % to 57·7 %(Reference Abbas, Adam and Rayis23,Reference Liabsuetrakul37) . Various Fe markers were reported in the reviewed studies, as previously mentioned. Out of six studies that measured Hb, three reported a significant positive association between overweight/obesity and Hb levels,(Reference Mayasari, Hu and Chao24,Reference Jones, Zhao and Jiang36,Reference Mocking, Savitri and Uiterwaal38) while the others found no association. Three (out of five) reported a decrease in serum Fe(Reference Scholing, Olthof and Jonker21,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Shin, Lee and Song40) among overweight/obese pregnant women, while the other studies demonstrated no association.(Reference Dao, Sen and Iyer34,Reference Garcia-Valdes, Campoy and Hayes41) . One out of three studies showed a decrease in TS%(Reference Mayasari, Hu and Chao24), while the others (n 2) showed no association(Reference Dao, Sen and Iyer34,Reference Garcia-Valdes, Campoy and Hayes41) . One study (out of seven) revealed a decrease in serum ferritin(Reference Abbas, Adam and Rayis23) among overweight/obese individuals, one showed an increase in serum ferritin(Reference Mayasari, Hu and Chao24) and the other five studies did not report any association(Reference Scholing, Olthof and Jonker21,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Jones, Zhao and Jiang36,Reference Rahma, Lumbanraja and Lubis39,Reference Shin, Lee and Song40) . Among the seven studies that measured serum ferritin, only two measured inflammatory markers(Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Jones, Zhao and Jiang36) . However, they did not adjust serum ferritin levels for the presence of inflammation. Lastly, one (out of three) study reported a significant increase in sTfR levels,(Reference Jones, Zhao and Jiang36) while the other two did not report any difference(Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Garcia-Valdes, Campoy and Hayes41) .
Only three studies measured CRP, and all of them reported an increased level in overweight/obese pregnant women(Reference Scholing, Olthof and Jonker21,Reference Dao, Sen and Iyer34,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35) . An increase in leptin and IL-6 levels was also found by Flores-Quijano et al. (Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35). However, one study reported that there was no difference in IL-6 levels between overweight/obese and normal weight(Reference Dao, Sen and Iyer34). Moreover, four out of five studies found significantly higher hepcidin in overweight/obese pregnant women compared to normal weight(Reference Mayasari, Hu and Chao24,Reference Dao, Sen and Iyer34,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Garcia-Valdes, Campoy and Hayes41) , while another one failed to find a significant association(Reference Rahma, Lumbanraja and Lubis39). In pregnant women, overweight/obesity was found to be associated with a significantly higher proportion of ID in two studies(Reference Abbas, Adam and Rayis23,Reference Mayasari, Hu and Chao24) . Despite this, out of the five studies that compared the proportion of anaemia between overweight/obesity and normal weight, four reported a lower proportion of anaemia among overweight/obese women(Reference Abbas, Adam and Rayis23,Reference Mayasari, Hu and Chao24,Reference Liabsuetrakul37,Reference Mocking, Savitri and Uiterwaal38) , while one reported no difference(Reference Jones, Zhao and Jiang36). Among three studies that measured IDA, two found a lower prevalence of IDA among overweight/obese women(Reference Tan, Qi and He22,Reference Mayasari, Hu and Chao24) , while the other found no association between overweight/obesity and IDA(Reference Abbas, Adam and Rayis23).
A retrospective cohort study in China found that underweight women had a higher risk of IDA compared to normal-weight women. Although the risk was lower in women with obesity, the higher rate of gestational weight gain was associated with a higher risk of IDA(Reference Tan, Qi and He22). Abbas et al. (Reference Abbas, Adam and Rayis23) in their study found a relationship between obesity and ID among pregnant women, where obese pregnant women had significantly lower serum ferritin levels (P = 0·010), and thus, had a higher proportion of ID (P = 0·015) than their counterparts. Surprisingly, the same study also reported a lower prevalence of anaemia (P < 0·001) in overweight/obese women without any significant difference in Hb concentration. In conclusion, overweight/obesity in pregnant women studies were generally associated with higher levels of Hb, hepcidin and inflammatory markers, and lower serum Fe, but not associated with the serum ferritin, serum TS% and sTfR. This resulted in a lower proportion of anaemia and IDA in overweight/obese pregnant women.
Outcomes in non-pregnant women participants
The extracted data for non-pregnant women are summarised in see online supplementary material, Supplementary Table 4. Prevalence of overweight/obesity ranged from 25·3 % to 70·78 %(Reference Herter-Aeberli, Thankachan and Bose18,Reference Karl, Lieberman and Cable27,Reference Kordas, Centeno and Pachón28,Reference Chang, Chen and Owaga44,Reference Eckhardt, Torheim and Monterrubio45,Reference Hiremath, Kumar and Huchchannavar47,Reference Hisa, Haruna and Hikita48,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) . Anaemia prevalence in non-pregnant women ranged from 10·0 % to 22·3 %(Reference Cheng, Bryant and Rooney25,Reference Kordas, Centeno and Pachón28,Reference Chang, Chen and Owaga44,Reference Hisa, Haruna and Hikita48,Reference Nainggolan, Hapsari and Titaley49) . Many studies used Hb as a proxy parameter to measure Fe status, and none of them demonstrated a significant decrease in Hb concentration among overweight/obese non-pregnant women. Instead, three studies reported inverse results, in which Hb levels increased among overweight/obese compared to normal-weight non-pregnant women(Reference Cheng, Bryant and Rooney25,Reference Kordas, Centeno and Pachón28,Reference Fricker, Le Moel and Apfelbaum46) . Seven out of twelve studies that measured Hb did not show any association between Hb and overweight/obesity(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25–Reference Karl, Lieberman and Cable27,Reference Rad, Sefidgar and Tamadoni42,Reference Hiremath, Kumar and Huchchannavar47,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) , three found an increase of Hb among overweight/obese women(Reference Kordas, Centeno and Pachón28,Reference Chang, Chen and Owaga44,Reference Fricker, Le Moel and Apfelbaum46) and the rest (n 2) did not test the association(Reference Hisa, Haruna and Hikita48,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50) . Out of nine studies that measured ferritin(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25–Reference Kordas, Centeno and Pachón28,Reference Beard, Borel and Peterson43,Reference Chang, Chen and Owaga44,Reference Fricker, Le Moel and Apfelbaum46,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50) , six studies revealed a positive association between overweight/obesity and serum ferritin levels(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25,Reference Kordas, Centeno and Pachón28,Reference Beard, Borel and Peterson43,Reference Chang, Chen and Owaga44,Reference Fricker, Le Moel and Apfelbaum46) . Among those six studies, three did not report any inflammatory marker(Reference Beard, Borel and Peterson43,Reference Chang, Chen and Owaga44,Reference Fricker, Le Moel and Apfelbaum46) , while others that measured inflammatory markers did not adjust ferritin for the presence of inflammation(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25,Reference Kordas, Centeno and Pachón28) . Only one study adjusted ferritin levels for inflammation status, which reported lower ferritin was associated with central adiposity as indicated by waist circumference (OR = 0·59 (0·38–0·91)) but not with BMI(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50).
A few other studies have used serum Fe (n 5), TS% (n 6) and sTfR (n 4) to measure Fe status. One study reported a decrease in serum Fe concentration in overweight/obese subjects(Reference Cheng, Bryant and Rooney25), while two reported the opposite(Reference Beard, Borel and Peterson43,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) and two did not find any association(Reference Chang, Chen and Owaga44,Reference Fricker, Le Moel and Apfelbaum46) . Higher serum TS% in overweight/obese non-pregnant women was only seen in one study(Reference Beard, Borel and Peterson43) out of six, whereas two reported lower serum TS%(Reference Cheng, Bryant and Rooney25,Reference Jordaan, Van den Berg and Van Rooyen26) . The rest of the studies (n 3) did not find any difference(Reference Karl, Lieberman and Cable27,Reference Fricker, Le Moel and Apfelbaum46,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) . Of the four studies that measured sTfR, one found an increase in sTfR among overweight/obese women(Reference Herter-Aeberli, Thankachan and Bose18), two showed no association(Reference Cheng, Bryant and Rooney25,Reference Karl, Lieberman and Cable27) and one did not compare sTfR between the groups(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50). Herter et al. in their study reported a higher hepcidin level in overweight/obese non-pregnant women(Reference Herter-Aeberli, Thankachan and Bose18), while the other study reported no association(Reference Cheng, Bryant and Rooney25). Higher CRP was reported among overweight/obese subjects in all six studies(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25,Reference Jordaan, Van den Berg and Van Rooyen26,Reference Kordas, Centeno and Pachón28,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) . Six out of seven studies assessing anaemia reported a lower proportion of anaemia among obese women(Reference Kordas, Centeno and Pachón28,Reference Chang, Chen and Owaga44,Reference Eckhardt, Torheim and Monterrubio45,Reference Hiremath, Kumar and Huchchannavar47–Reference Nainggolan, Hapsari and Titaley49) , while the other reported no difference in the anaemia proportion(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50). ID was measured in four studies, two of which reported a lower prevalence of ID among overweight/obese women(Reference Karl, Lieberman and Cable27,Reference Chang, Chen and Owaga44) , one of which reported a higher prevalence(Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) and one of which found no association(Reference Kordas, Centeno and Pachón28). IDA was reported in only one study in which a lower prevalence of IDA was found among obese women(Reference Chang, Chen and Owaga44).
In a study among non-pregnant Cuban, anaemia was associated with ID (OR: 3·02 (1·82–5·03)) and erythropoietic deficiency (sTfr >8·3 µg/ml) (OR: 5·62 (3·03–10·39)), but not with overweight (OR:0·80 (0·57–1·12)), central adiposity (OR:0·80 (0·57–1·12)) and inflammation (OR: 1·00 (0·65–1·54))(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50). The study also reported that overweight/obesity was correlated with elevated CRP levels (OR: 3·06 (1·89–4·94)) rather than with AGP levels (OR: 1·80 (1·05–3·08))(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50). In conclusion, studies among non-pregnant women revealed that overweight/obesity was generally associated with higher ferritin and serum Fe levels but not with Hb, sTfR, TS% and total iron-binding capacity concentrations. An increased level of CRP, AGP and hepcidin among obese non-pregnant women were also reported in most of the studies. Overweight/obese non-pregnant women also had a lower prevalence of anaemia and IDA, but the association with ID was mixed.
Discussion
To the best of our knowledge, this systematic review is the first study addressing literature reporting the association of overweight/obesity with Fe status and anaemia in WRA, including pregnant women. Overall, the majority of the studies (pregnant and non-pregnant women) have demonstrated a significant inverse association between overweight/obesity and the prevalence of anaemia and IDA. On the other hand, there was a higher prevalence of ID among overweight/obese women. When looking at the individual Fe biomarkers to assess Fe status in the included studies, overweight/obesity was positively associated with the concentrations of serum ferritin, hepcidin and other inflammation markers. Still, it was negatively associated with serum Fe. Overall, no association was found between sTfR, TS% and Hb in WRA.
Although serum ferritin is the most reliable and commonly used biomarker for ID at the population level(52), it is also an acute-phase protein that increases (irrespective of Fe status) in the presence of subclinical infection or inflammation, including chronic inflammation caused by excessive body fat. Of note, earlier studies have demonstrated a correlation between increased serum ferritin and CRP levels, a metric for measuring systemic inflammation, particularly during the start of an infection or inflammatory response(Reference Herter-Aeberli, Thankachan and Bose18,Reference Fitzsimons and Brock53,Reference Baynes, Bezwoda and Bothwell54) . Thus, higher serum ferritin among overweight/obese women, reported in this review, does not necessarily reflect an actual increase in Fe storage but could be the result of low-grade, chronic inflammation associated with overweight/obesity(Reference Cheng, Bryant and Cook29,Reference Moisidis-Tesch and Shulman55,Reference England, Ward and Down56) . To assess Fe status using serum ferritin levels, adjustment for subclinical infection/inflammation is required using inflammatory markers such as CRP and AGP(Reference Namaste, Rohner and Huang57,58) . Although nine of twenty-seven primary studies measured the CRP levels, only one study adjusted the serum ferritin concentration for high CRP levels(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50). This study revealed that ID was not associated with being overweight but associated with central obesity as indicated by waist circumference(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50). Adiposity was associated with inflammation as indicated by increased CRP and AGP(Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50). Hence, future studies exploring the association between overweight/obesity and Fe status should consider assessing Fe biomarkers along with inflammatory markers to adjust for subclinical infection or inflammation while assessing Fe status.
sTfR is an Fe-binding protein crucial for transporting Fe to the target tissues(Reference Clucas, Biggs, Karakochuk, Zimmermann, Moretti and Kraemer59). Thus, an increase in sTfR reflects insufficient Fe stores and indicates Fe-deficient erythropoiesis. Among six studies that examined the association between overweight/obesity and sTfR(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25,Reference Karl, Lieberman and Cable27,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Jones, Zhao and Jiang36,Reference Garcia-Valdes, Campoy and Hayes41) , two reported a significant increase in sTfR among overweight/obese pregnant and non-pregnant women(Reference Herter-Aeberli, Thankachan and Bose18,Reference Jones, Zhao and Jiang36) . It is noteworthy that, unlike serum ferritin, sTfR remains unaffected by inflammation and thus remains a more reliable marker of Fe storage if inflammation markers are not measured(52). Therefore, the studies that found overweight/obesity is associated with lower Fe storage (higher sTfR) support the known pathway of overweight/obesity and IDA. However, the majority of the studies found no association between overweight/obesity and sTfR levels. Karl et al. explained that a certain critical level of body fat in overweight/obese individuals may contribute to an increase in sTfR and support the overweight/obese–IDA relationship(Reference Karl, Lieberman and Cable27).
Decreased serum Fe was observed among overweight/obese pregnant and non-pregnant women in four studies(Reference Scholing, Olthof and Jonker21,Reference Cheng, Bryant and Rooney25,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35,Reference Shin, Lee and Song40) implying less Fe accessible(Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35). Moreover, decreased serum Fe could be explained by the increased inflammation among the overweight/obese population. When inflammatory cytokines are released, Fe is more readily taken up and retained by reticuloendothelial system cells, preventing it from being transported to the bone marrow for erythropoiesis(Reference Pfeiffer and Looker60). Alternatively, decreased serum Fe could also be explained by haemodilution in overweight/obese women, which can occur due to an increase in total blood volume(Reference Cepeda-Lopez, Zimmermann and Wussler61). During transport, Fe is bound to transferrin, and the level of transferrin bound to Fe (or TS%) can be considered as an indicator of circulating Fe(Reference Brugnara and Means62). During the second and third stages of ID, TS% decreased along with serum Fe and ferritin. Overall, only a few studies reported a lower TS% among overweight/obese WRA(Reference Mayasari, Hu and Chao24–Reference Jordaan, Van den Berg and Van Rooyen26). The pathway responsible for the decrease in TS% among overweight/obese might be attributable to impaired Fe absorption, which is consistent with persistently present low-grade inflammation and hepcidin elevation among overweight/obese individuals(Reference Herter-Aeberli, Thankachan and Bose18,Reference Ganz63) .
Hepcidin is one of the most significant markers of Fe status, which plays a role in Fe metabolism(Reference Nemeth and Ganz64). Hepcidin controls the amount of Fe in the blood by binding to ferroprotein, which causes ferroprotein to be internalised and degraded and prevents cellular Fe transfer into plasma(Reference Nemeth, Tuttle and Powelson65). Ferroprotein is an Fe exporter on the surface of absorptive intestinal enterocytes, hepatocytes, macrophages and placental cells, all of which release Fe into the plasma(Reference Nemeth, Tuttle and Powelson65). Hepcidin also delays the release of recycled Fe from macrophages to the periphery and the mobilisation of Fe from the liver or spleen reserves(Reference Alshwaiyat, Ahmad and Wan Hassan66,Reference Tussing-Humphreys, Pustacioglu and Nemeth67) . During the second and third stages of ID, hepcidin release is decreased as a feedback mechanism, especially during erythropoiesis, to ensure Fe availability for Hb synthesis(Reference Nemeth and Ganz64). This is also observed during pregnancy to meet the increased Fe requirements of the mother and the fetus irrespective of overweight/obesity(Reference Nemeth, Tuttle and Powelson65). In addition, hepcidin is also produced in small amounts by adipose tissue and is higher in overweight/obese individuals(Reference Reichert, da Cunha and Levy68), which could also be attributed to inflammation related to overweight/obesity through the JAK/STAT pathway(Reference Cepeda-Lopez, Aeberli and Zimmermann30). This phenomenon was also seen in all the included studies (n 4) that measured hepcidin as they showed an association between overweight/obesity and hepcidin(Reference Herter-Aeberli, Thankachan and Bose18,Reference Mayasari, Hu and Chao24,Reference Dao, Sen and Iyer34,Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35) . This means overweight/obesity and hepcidin contribute to the mechanism during the Fe-deficient stage. Supporting this finding, a study in Taiwanese pregnant women proved that hepcidin levels were significantly higher in obese pregnant women until third trimester regardless of Fe status(Reference Mayasari, Hu and Chao24). Thus, the result of this review emphasises the need for hepcidin measurement in overweight/obese and IDA association.
An association between overweight/obesity and IDA is hypothetically attributed to adiposity-induced low-grade inflammation due to cytokine production(Reference Wawer, Hodyl and Fairweather-Tait20). Elevated inflammatory cytokines such as CRP, AGP and IL-6 levels have also been reported among pregnant women with overweight/obesity compared to their counterparts(Reference Dao, Sen and Iyer34–Reference Jones, Zhao and Jiang36), as well as in overweight/obese non-pregnant women(Reference Herter-Aeberli, Thankachan and Bose18,Reference Cheng, Bryant and Rooney25,Reference Jordaan, Van den Berg and Van Rooyen26,Reference Kordas, Centeno and Pachón28,Reference Pita-Rodríguez, Basabe-Tuero and Díaz-Sánchez50,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) . Acute-phase proteins such as CRP and AGP increase during infection or inflammation. They are often used collectively with markers of Fe status, such as serum ferritin, to help interpret the results(Reference Clucas, Biggs, Karakochuk, Zimmermann, Moretti and Kraemer59). CRP was the most frequently reported inflammation marker in the reviewed papers and levels aligned with obesity-related inflammation. Additionally, a relationship between CRP and ferritin in overweight/obese women found in one of the included studies(Reference Herter-Aeberli, Thankachan and Bose18), as well as the association between IL-6 and ferritin(Reference Flores-Quijano, Vega-Sánchez and Tolentino-Dolores35), strengthen the proposed pathway, which explicitly explains the role of adiposity in the relationship between inflammation and increased ferritin due to adiposity(Reference Cheng, Bryant and Cook29).
Hb level is the key indicator to define anaemia, which occurs when Hb concentration is less than a specified cut-off point based on gender, age, physiological status, smoking habits and altitude(12). Based on the known pathway of overweight/obese-related IDA, the Hb level should be decreased due to inhibited Fe absorption(Reference Wawer, Hodyl and Fairweather-Tait20,Reference Tussing-Humphreys, Pustacioglu and Nemeth67) . Several studies used Hb as a proxy indicator to define IDA. However, the majority of the studies failed to find an association between overweight/obesity and Hb levels; although in pregnant women, most of the studies reported a higher Hb among overweight/obese individuals(Reference Mayasari, Hu and Chao24,Reference Jones, Zhao and Jiang36,Reference Mocking, Savitri and Uiterwaal38) . None of the studies that reported increased Hb explained their findings. In a separate report, it was explained that obese people were more likely to have co-morbid conditions such as persistent tissue hypoxia because of sleep apnoea causing polycythaemia and increasing Hb(Reference Cheng, Bryant and Cook29). Moreover, higher Hb may also be mediated by higher Fe intake in overweight/obese women(Reference Qin, Melse-Boonstra and Pan69). Unfortunately, this systematic review failed to confirm the assumption since none of our included studies measured Fe intake among WRA. Menzie et al., in their research, reported higher heme-iron and animal protein intake but lower vitamin C and Ca intake among obese adults than non-obese counterparts(Reference Menzie, Yanoff and Denkinger70). However, dietary differences were not associated with the inverse association between obesity and low serum Fe.
Even though most of the included studies measured Hb or ferritin as Fe status markers, very few categorised these variables into anaemia, ID or IDA and compared overweight/obesity and normal weight WRA. Overall, most of the studies that compared these parameters reported a lower prevalence of anaemia and IDA in overweight/obese WRA. On the other hand, ID was higher among overweight/obese WRA in most of the studies, including studies in pregnant women. The differences in results between different studies could be due to using different markers to define ID, such as unadjusted ferritin(Reference Abbas, Adam and Rayis23,Reference Karl, Lieberman and Cable27,Reference Chang, Chen and Owaga44) , TS%(Reference Mayasari, Hu and Chao24,Reference Karl, Lieberman and Cable27,Reference Chang, Chen and Owaga44,Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51) , serum Fe(Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51), total iron-binding capacity(Reference Cepeda-Lopez, Osendarp and Melse-Boonstra51), Red Cell Distribution Width (RDW)(Reference Karl, Lieberman and Cable27) or a combination of these. Additionally, confounding factors such as age, education, menstrual loss and dietary intake, as well as parity and gestational age(Reference Karakochuk, Zimmermann and Moretti71) in pregnant women, were not considered in many included studies. Moreover, the small sample sizes may have contributed to these inconclusive results. The degree to which overweight/obesity-related inflammation alters Fe metabolism is yet unknown; thereby, it is challenging to come to a uniform conclusion regarding the association between overweight/obesity and IDA in WRA. Measuring inflammatory markers with hepcidin and Fe status markers is crucial to understanding the relationship between overweight/obesity and IDA. However, only a few studies (four out of twenty-seven) simultaneously measured Fe status, hepcidin and inflammation markers, limiting our ability to discuss the results and draw conclusions.
Despite some limitations in this review, including a limited measure of confounding factors, dietary intake assessment and a limited number of studies that adjust ferritin for inflammation, this review has several strengths. This review included the broader use of Fe status markers, including hepcidin and various inflammation markers (CRP, AGP and IL-6), which were not addressed much in the previous systematic review. Moreover, this review also explored the association in a specific population group, such as WRA aged 18–50 years, which added to the body of knowledge in the field.
Conclusion
Despite the limitations of the selected articles in reporting Fe status markers altogether with hepcidin and inflammatory markers to substantiate the relation between overweight/obesity and IDA, this review demonstrates that overweight/obese WRA tend to exhibit a lower prevalence of anaemia and IDA but a higher prevalence of ID. We also demonstrated overweight/obese WRA had higher hepcidin and inflammatory markers, but there was no significant overall association between Hb, sTfR and TS% with overweight/obesity. Nevertheless, this systematic review captures the lack of well-designed studies to explain the association between overweight/obesity and IDA among WRA, including relatively few longitudinal studies. Our review underscores the urgent need for further studies to be carried out more comprehensively, considering a set of Fe markers, hepcidin and inflammation markers, as well as potential confounders, particularly dietary Fe intake or Fe supplementation, that could highly influence the association between overweight/obesity and IDA.
Acknowledgements
The authors would like to thank Pusat Layanan Pembiayaan Pendidikan (PUSLAPDIK) Ministry of Education, Culture, Research and Technology, Lembaga Pengelola Dana Pendidikan (LPDP) Ministry of Finance, Indonesia, and Universitas Airlangga for funding this study. The authors also thank Heather Monro-Allison of the Griffith University Library for her assistance in the literature search.
Financial support
This study is supported by Pusat Layanan Pembiayaan Pendidikan (PUSLAPDIK) Ministry of Education, Culture, Research and Technology and Lembaga Pengelola Dana Pendidikan (LPDP) Ministry of Finance, Indonesia, and Universitas Airlangga.
Conflict of interest
None.
Authorship
Q.R., F.A. and H.P. contributed to conceptualising the project. Q.R. and P.M. performed article screening, data extraction and interpretation. Q.R. wrote the drafts of the document. P.M., F.A. and H.P. revised the document. All authors contributed to the final approval of the version to be published.
Ethics of human subject participation
Not applicable.
Supplementary material
For supplementary material accompanying this paper visit https://doi.org/10.1017/S1368980024001794
Open access
