Skip to main content
Download PDF
  • Systematic Review
  • Open access
  • Published:

Association of dietary diversity and odds of anemia in children and adolescents: a systematic review and meta-analysis of observational studies

BMC Nutrition volume 11, Article number: 83 (2025) Cite this article

Abstract

Background

Anemia, characterized by a deficiency in red blood cells or hemoglobin, remains a significant public health concern worldwide, particularly among children and adolescents. Inadequate dietary intake, including micronutrient deficiencies, has been associated with anemia. Dietary diversity, characterized by the consumption of a variety of food groups, may contribute to adequate iron intake and a reduced likelihood of anemia. This systematic review and meta-analysis examined the association between dietary diversity and odds of anemia among children and adolescents.

Methods

A comprehensive search of electronic databases (PubMed, Web of Science, Scopus) was conducted for observational studies (cross-sectional or case–control) published before April 2024 that assessed the association between dietary diversity and anemia among children and adolescents. The odds of Bias in Non-Randomized Studies of Exposures (ROBINS-E) tool was used to assess the quality of included studies, ensuring a standardized and rigorous evaluation process. Subgroup analyses explored potential variations in this association based on age group, geographic location, and type of anemia.

Results

Nineteen studies (18 cross-sectional and 1 case–control) examined the association between dietary diversity and anemia in children under 5 years old. Pooled analysis revealed a significant association between lower dietary diversity and higher odds of anemia among children aged 0 to 5 years (OR = 1.96; 95% CI: 1.57, 2.45; I2 = 83.6%, τ2 = 0.38 P < 0.001). Ten studies examined the relationship in children and adolescents aged 6–18 years, showing a similar pattern (OR = 1.73; 95% CI: 1.27, 2.36; I2 = 87%, τ2 = 0.44; P < 0.001). Subgroup analyses suggested that the association varied across specific geographic regions.

Conclusions

This meta-analysis indicates a significant association between lower dietary diversity and higher odds of anemia in children and adolescents. These findings underscore the importance of dietary diversity as a potential factor related to anemia prevalence. Future research should focus on standardizing dietary diversity assessment methods and incorporating detailed dietary quality measurements.

Peer Review reports

Introduction

Micronutrient deficiencies are prevalent globally and are associated with child mortality in some poor and less developed countries and regions such as sub-Saharan Africa [1, 2]. Anemia is a micronutrient deficiency disorder with significant public health implications worldwide [3]. It is commonly observed among children and has been linked to health and development [4]. These impacts include higher rates of growth retardation, reduced immunity and cognitive abilities, and potential adverse effects on health extending into adulthood. Also, anemia can lead to a cascade of detrimental health consequences, including fatigue, decreased physical work capacity, significantly impacting a child's overall well-being and developmental trajectory [5, 6].

Iron deficiency anemia (IDA), characterized by a lack of red blood cells or hemoglobin, remains a significant public health concern worldwide [7, 8]. This condition disproportionately affects children and adolescents, particularly those residing in low- and middle-income countries [9, 10]. In addition to iron deficiency, anemia has been associated with other nutritional inadequacies such as protein, B vitamins, and zinc deficiencies [11,12,13].

According to the World Health Organization (WHO), anemia is defined by hemoglobin levels < 11.0 g/dL for children under 5 years, < 11.5 g/dL for children 5–11 years, and < 12 g/dL for children 12–14 years [14]. Notably, while anemia can occur across the lifespan, young children under two years old are particularly vulnerable [13, 15, 16]. This heightened susceptibility is attributed to rapid physical and mental development that increases dietary demands. Global estimates suggest that a staggering 269 million children suffered from anemia in 2019 [17, 18]. This burden is not evenly distributed, with approximately two-thirds of these cases concentrated in Asia and Africa. Furthermore, sub-Saharan Africa exhibits the most concerning prevalence rates, with childhood anemia affecting an estimated 46% to 66% of the population [17, 19].

Dietary diversity, defined as the consumption of a variety of food groups and micronutrients, plays a crucial role in ensuring adequate intake of vitamins and minerals [20]. A diet with greater diversity has been linked to higher micronutrient intake, which may contribute to improved iron status and lower odds of anemia [8, 21,22,23]. Conversely, limited dietary variety can lead to micronutrient deficiencies, especially iron deficiency, increasing susceptibility to anemia [24, 25].

While previous studies have explored the association between dietary diversity and anemia in children, the existing evidence comes from individual studies with potentially limited generalizability. Furthermore, the relationship between dietary diversity and anemia might vary depending on factors like age group, geographic location, and socioeconomic status. There is a need for a more comprehensive analysis to elucidate these connections.

This systematic review and meta-analysis aimed to address this gap in knowledge. By systematically searching and analyzing data from multiple observational studies, we aimed to provide a more robust understanding of the relationship between dietary diversity and anemia in children and adolescents.

Method

Data source and search strategy

The present study was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered in Prospero database.

(CRD42024545540). We carried out a comprehensive systematic search from inception to April 2024 in electronic databases including PubMed, Web of Science, and Scopus without any date or language restrictions. Dietary diversity, Dietary diversity score, Anemia, and Iron Deficiency Anemia (Supplementary Table 1) were used in the search approach. In addition, the reference lists of the retrieved articles, google scholar and related review studies were manually searched.

Eligibility criteria

  • Population (P): Children and adolescents aged 0–18 years.

  • Intervention (I): Assessment of dietary diversity as an exposure factor.

  • Comparison (C): Higher dietary diversity vs. lower dietary diversity.

  • Outcome (O): Odds of anemia, including iron deficiency anemia.

  • Study Design (S): Observational studies (cross-sectional and case–control studies).

Studies meeting these criteria were included in the systematic review and meta-analysis. Exclusion criteria comprised studies focusing on adults, maternal or paternal dietary diversity, lack of anemia outcome data, and study designs such as editorials, commentaries, conference papers, reviews, letters to the editor, unpublished studies, or abstracts. A detailed summary of inclusion and exclusion criteria is provided in Supplementary Table 2.

Study selection

Two independent reviewers (MR and SPM) screened the eligible articles. At first, title/abstract of articles were scanned, publications that were irrelevant were excluded. Then, remaining records were assessed for eligibility using full text. Any disagreement was resolved by consensus with a third investigator (NGh).

Data collection

The following information were extracted from each included study by two independent investigators (MR and SPM): the first author’s last name, research publication year, study country of origin, study design, sample size, participants'gender and age range, dietary measurement, relevant effect estimates, anemia case count, outcome assessment method, and any adjusted analyses. Disagreements were resolved by a third reviewer (NGh). We used the adjusted measure if both adjusted and unadjusted effect sizes were reported.

Quality assessment

The quality of included studies was assessed using the ROBINS-E tool, which evaluates study quality based on bias domains, including confounding, selection, classification, missing data, measurement, and reporting bias. Studies were categorized as having low, moderate, or high risk of bias based on their ROBINS-E scores.

Confounding

Most studies adjusted for common confounders, such as age, sex, socioeconomic status, and physical activity. However, residual confounding remained a significant concern, particularly in cross-sectional studies, which cannot fully control for all potential confounders. In many studies, the adjustments for confounding were incomplete or inadequately addressed, resulting in a classification of some concern or high risk of bias for confounding.

Selection bias

Selection bias was evaluated based on the sampling methods used in each study. Many studies relied on convenience sampling, which may lead to a non-representative sample. In studies where the sample was more randomly selected or better matched, the risk was rated as some concern. However, due to the common use of convenience sampling in cross-sectional designs, some studies were rated as high bias in this domain.

Classification of exposure

Exposure classification in many studies relied on self-reported dietary intake, often using a single 24-h recall, which is prone to recall bias and measurement error. As a result, studies relying on this method were rated as high or very high bias in this domain. Studies that used multiple 24-h recalls or more accurate exposure assessment methods (e.g., food diaries) were rated with some concern for bias.

Deviation from intended interventions

Since the studies included in this review were observational, this domain was not applicable. No interventions were planned or administered, and as such, there was no deviation from intended interventions.

Missing data

Missing data was a concern in some studies, with incomplete participant data or a high rate of drop-out. Some studies provided clear information on how missing data were handled or imputed, they were rated as low bias. Studies without proper handling of missing data were rated as having some concern or high bias.

Outcome measurement

Outcome measurement was evaluated based on whether the outcome assessors were blinded to exposure status and whether standardized and validated measurement tools were used. In many studies, it was unclear whether assessors were blinded, and some studies used unvalidated or subjective outcome measures. As a result, the majority of studies were rated as having some concern or high bias in this domain.

Selective reporting

Selective reporting of outcomes was assessed by examining whether all pre-specified outcomes were reported. Some studies selectively reported outcomes or failed to report all planned outcomes, leading to some concern or high-risk ratings in this domain. A detailed bias assessment for each study across these domains is provided in Supplementary Table 3.

Statistical analysis

Statistical analyses were performed with STATA (version 14; Stata Corporation, College Station, TX). To assess the association between dietary diversity and anemia, the estimates for anemia and IDA were pooled. The pooled odds of anemia among the children and adolescents was calculated using log OR and 95% CI were estimated using a DerSimonian and Laird weighted random effects model methods. To assess the heterogeneity across the included studies, we used the I2 statistic, which quantifies the percentage of total variation across studies due to heterogeneity, and tau-squared (τ2), which provides an absolute measure of variance in true effect sizes. I2 values of 0–25%, 25–50%, 50–75%, and 75–100% were considered to represent low, moderate, high, and very high heterogeneity, respectively. Tau-squared values were derived from a random-effects model using STATA version 14.

In addition to confidence intervals, we calculated 95% prediction intervals (PI) to provide an estimate of the expected range of effect sizes in future studies, incorporating between-study variance. Prediction intervals were computed using the following formula:

$$PI=\text{Pooled Effect }\pm 1.96 \times \sqrt{{r}^{2}+ {SE}_{pooled}^{2}}$$

where Ï„2 represents the between-study variance, and SE_pooled is the standard error of the pooled effect size, estimated from the confidence intervals.

To improve clinical interpretability, we also calculated absolute risk increases (ARI) for each population group, based on estimated baseline anemia prevalence. ARI was calculated using the formula:

$$\text{ARI}=\text{Baseline Risk }\times \left(\text{OR}-1\right)$$

where Baseline odds were derived from WHO estimates and included study data. Certainty of the evidence was assessed using the GRADE approach, starting at'low'due to the observational nature of the included studies. We systematically considered five domains—risk of bias, inconsistency, indirectness, imprecision, and publication bias—for possible downgrading. Although some effect sizes were moderate to large, we did not apply any upgrades due to the high risk of bias, substantial heterogeneity, and lack of dose–response assessment across studies. The overall certainty of evidence was rated as'low'for both age groups.

To identify the sources of heterogeneity, subgroup analyses were conducted based on continent (Africa, and Asia), type of anemia (anemia, Iron deficiency anemia), sex and ROBINS-E score (Low odds of bias vs. high odds of bias). Publication bias was assessed by using the Egger’s regression test and Begg’s test (significance point at P < 0.05, for all). Sensitivity analysis was conducted to examine the influence of each study on the overall effect size. In addition to Egger’s and Begg’s tests for publication bias, we compared the results of the random-effects model with the fixed-effects model as an exploratory analysis to evaluate the presence of small-study effects. A substantial difference in effect estimates between the two models may suggest small-study effects. Egger’s test was conducted using a p-value threshold of 0.05, which is a more conservative approach compared to the original threshold of 0.1. However, considering the observed visual asymmetry in the funnel plot, we performed additional sensitivity analyses to further explore potential publication bias.

Results

Characteristics of the included studies

The process of study selection is demonstrated in PRISMA flow diagram (Fig. 1). The initial literature search yielded 898 studies. After excluding duplicate records, 519 publications remained. Then, the title and abstract of these studies was screened and 473 article were excluded based on the study inclusion criteria. The full-text of 46 articles were reviewed to confirm eligibility. Of these, leading to the exclusion of 17 articles due to various reasons (Supplementary Table 2). Finally, 29 studies were included in the current systematic review and meta-analysis [24, 10, 26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52].

Fig. 1
figure 1

PRISMA flowchart of the studies examined and included in the meta-analysis

Full size image

The detailed characteristics of the included studies are summarized in Table 1. Of 29 included observational studies, 28 were cross-sectional in design [24, 26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44, 10, 46,47,48,49,50,51,52], and only 1 was case–control [45]. Studies were published between 2015 and 2024, with a sample size ranging from 210 to 14,669 participants. Seventeen studies conducted in Ethiopia [10, 27,28,29,30,31,32,33, 35, 40, 41, 43, 47,48,49,50,51], and the rest were performed in India (n = 4) [34, 36, 38, 44], Tanzania (n = 2) [39, 46], Nepal (n = 1) [37], Chad (n = 1) [52], Burkina Faso (n = 1) [42], Indonesia (n = 1) [26], Ghana (n = 1) [45], South Africa (n = 1) [24]. Some studies were conducted on both genders [24, 34,35,36, 10, 38,39,40,41,42,43,44,45,46,47,48,49, 51,52,53,54], while other studies were performed on girls only [26, 31, 33, 37, 50]. Dietary assessment was done with various methods including, 24-h recall [28, 30, 31, 33,34,35,36,37, 39, 42, 43], 7-day recall [52], 24 h and 7 days’ child food frequency recall [10], FFQ + 24-h recall [27, 32], 2-day 24-h recall [26], 3-day 24-h recall [24], and 30 days- FFQ [40, 41, 44]. Five studies did not report the dietary assessment method [29, 36, 38, 46, 49]. To assess dietary diversity, most of the studies used dietary diversity score (DDS) [24, 32,33,34,35,36,37,38,39,40, 42,43,44,45, 47,48,49,50,51,52], one study determined dietary diversity based on principal component analyses [41], and one study did not report the dietary diversity assessment method [46]. Anemia, and iron deficiency anemia were identified based on WHO reference values (2011).

Table 1 Characteristics of studies included in the systematic review and meta-analysis
Full size table

Included studies were reported their results as adjusted ORs and considered confounders such as child’s age [24, 26,27,28,29,30, 35,36,37, 40, 42, 43, 46,47,48, 50, 51], grade of child [29, 37], child’s sex [24, 28, 34,35,36, 42, 43, 51], mother’s education [29, 35,36,37, 40, 42, 43, 10, 48, 50, 51], mothers’ age [27, 28, 36, 40], father’s education [10, 29, 37, 42], father’s occupation [28, 37, 40], education of adolescent [26], knowledge on anemia [37], knowledge on weekly iron folic acid supplementation [37], iron folate utilization [41, 47, 48], iron intake [26], vitamin A rich food [52], dairy/egg intake [52], number of under five children within household [40], family size [28, 31, 47, 50], wealth index [31, 42, 43, 47, 51], monthly income [27, 30, 48], household food insecurity [27, 28, 33, 34, 41, 43, 47, 48], introduction time of complementary feeding [27, 28, 40, 41, 43, 47, 51], exclusive breast feeding [30], stunting [32, 43, 47, 48, 51], underweight [32, 43, 46, 48], diarrhea in the last 2 months [30, 43, 48, 51], growth monitoring service utilization [43, 48], birth interval [43], stool disposal [46], meal frequency of child [10, 31, 43], frequency of meat consumption in child [50], meal frequency of mother [28], intestinal parasites [27], menses status [31, 50], frequency of changed pad [31], duration of menses [31], Tea/coffee intake within 30 min after meal [29, 32, 50], wasting [29, 31, 50], receiving anti-helminthic drug [28], mother’s smoking, [49] source of drinking water [33, 40], and use of social media by adolescent [26]. One study not reported adjusted variables [45]. In 3 investigations, no adjustments for potential confounders were done [38, 39, 44].

The odds of bias ratings by ROBINS-E domain are presented in Supplementary Table 3. Except four studies that had some concerns, others studies rated with high bias (19 studies) and very high bias (6 studies). Most studies had high or very high odds of bias due to reliance on single 24-h recalls, which may introduce measurement error and misclassification bias. Also, residual confounding was a common issue, as many studies did not adjust for all key factors such as socioeconomic status and household food security. Moreover, few studies used validated dietary assessment tools, increasing the odds of exposure misclassification.

Dietary diversity and odds of anemia among 0 to 5 years old children

Nineteen studies with 19 effect size were evaluated the association between dietary diversity and odds of anemia. Based on pooled results, there was a significant correlation between inadequate dietary diversity and odds of anemia among the children under 5 years old (OR = 1.96; 95% CI: 1.57, 2.45; I2 = 83.6%, τ2 = 0.38 P < 0.001) (Fig. 2). The 95% prediction interval was [0.57, 6.70], suggesting that future studies might observe varying effect sizes within this range. Based on the estimated baseline anemia prevalence of 40% in this age group, the absolute effect suggests ~ 384 additional anemia cases per 1,000 children due to inadequate dietary diversity. Certainty of evidence was rated as low due to study limitations (high risk of bias), inconsistency (I2 > 80%), and imprecision (wide prediction intervals). No upgrade was applied (Table 2).

Fig. 2
figure 2

Forest plot showing odds ratio with 95% confidence interval of the association between dietary diversity and odds of anemia among 0 to 5 years old children

Full size image
Table 2 Summary of findings using the GRADE approach
Full size table

Potential sources of variation were evaluated by subgroup analysis; There was no heterogeneity in studies conducted in Asia. Moreover, Subgroup analysis suggested that there was a significant association between inadequate dietary diversity and an enhanced prevalence of anemia for 0 to 5 years old children with overall anemia prevalence (vs. iron deficiency anemia) (OR = 2.19; 95% CI: 1.66, 2.86; I2 = 82.7%, τ2 = 0.36, P = < 0.001; n = 16). Moreover, we found a significant heterogeneity among the studies with high or very high odds of bias (Table 3). Based on the visual inspection of funnel plot, we found an asymmetry; however, when we did Begg (P = 0.135) and Egger's regression tests (P = 0.071), no significant publication bias was seen (Fig. 3). Sensitivity analysis showed that the overall effect size regarding the association between inadequate dietary diversity and odds of anemia among 0 to 5 years old children did not depend on single study (CI range: 1.28, 2.95).

Table 3 Subgroup analyses for the association between dietary diversity and anemia among 0 to 5 years old children
Full size table
Fig. 3
figure 3

Funnel plot representing publication bias in the studies reporting the association between dietary diversity and anemia among 0 to 5 years old children

Full size image

Dietary diversity and odds of anemia among 6 to 18 years old children and adolescents

Ten studies examined the relationship between insufficient dietary diversity and the prevalence anemia among children and adolescents aged 6 to 18 years. It has been demonstrated a significant association between inadequate dietary diversity and odds of anemia, with a significant heterogeneity (OR = 1.73; 95% CI: 1.27, 2.36; I2 = 87%, τ2 = 0.44; P < 0.001) (Fig. 4). The 95% prediction interval was [0.45, 6.58], indicating that future studies might find effect sizes within this range. Using a baseline anemia prevalence of 25% in this age group, the absolute effect suggests ~ 183 additional anemia cases per 1,000 children due to inadequate dietary diversity (GRADE: Low certainty).

Fig. 4
figure 4

Forest plot showing odds ratio with 95% confidence interval of the association between dietary diversity and anemia of among 6 to 18 years old children

Full size image

In the subgroup analysis based on sex, place of study and type of anemia, we couldn’t find the source of heterogeneity. Moreover, subgroup analysis revealed that there was a significant association between inadequate dietary diversity and an increased prevalence of anemia for 6 to 18 years old children and adolescents in studies performed in Africa continent (vs. Asia) (Table 4).

Table 4 Subgroup analyses for the association between dietary diversity and anemia among 6 To 18 years old children and adolescents
Full size table

Also, the leave-one-out sensitivity analysis showed that leaving each of studies had no significant effect on the pooled effect size. Although visually a small asymmetry was seen in the funnel plot, the results of Egger (P = 0.376) and Begg's test (P = 0.156) showed no evidence of publication bias (Fig. 5).

Fig. 5
figure 5

Funnel plot representing publication bias in the studies reporting the association between dietary diversity and anemia among 6 to 18 years old children

Full size image

Exploratory analysis for small-study effects

To further explore small-study effects, we compared the results from the fixed-effects model with the random-effects model. For children aged 0–5 years, the random-effects model yielded an OR = 1.96 (95% CI: 1.57, 2.45), while the fixed-effects model resulted in an OR = 1.77 (95% CI: 1.49, 2.33). Similarly, for children aged 6–18 years, the random-effects model showed an OR = 1.73 (95% CI: 1.27, 2.36), whereas the fixed-effects model yielded an OR = 1.50 (95% CI: 1.21, 2.24).

The observed differences between the models suggest that smaller studies may have contributed to the heterogeneity in effect sizes. This finding, combined with visual asymmetry in the funnel plot, underscores the need for cautious interpretation of potential small-study effects.

Discussion

The present systematic review and meta-analysis investigated the association between dietary diversity and the prevalence of anemia among children and adolescents. The results indicate a significant positive correlation between inadequate dietary diversity and increased odds of anemia in both age groups examined (0–5 years and 6–18 years). These findings underscore the importance of dietary diversity and its association with anemia, a prevalent nutritional deficiency with significant health implications for young populations.

Anemia is still a significant public health issue in most low-income and less developed countries, especially some African countries [55]. Anemia remains a major public health concern in sub-Saharan Africa, as demonstrated by the high prevalence rate of 72.0% [95% CI: 70.9%, 72.9%] among children aged 6–23 months [56]. One potential reason could be the persistent issue of malnutrition among children in these countries, stemming from inadequate dietary intake of essential nutrients. Several previous studies have shown an association between insufficient dietary diversity and nutrition-related diseases. Some of these studies have reported an inverse association between dietary diversity and child stunting in among the children in children in Cambodia [57] and Bangladesh [58]. However, contrary to these findings, Ali and colleagues discovered that dietary diversity did not influence the relationship between household food security and child stunting or wasting in any of the four countries they examined [59]. The comparability of these findings is hindered by various methodological challenges associated with assessing dietary diversity.

A growing body of research indicates a concerning trend in children's diets: they are often high in calorie-dense foods but lacking in essential micronutrients [60,61,62]. Among all age groups, children under five exhibit the greatest vulnerability to chronic malnutrition and its associated micronutrient deficiencies [63]. Several studies have established a link between limited dietary diversity and micronutrient deficiencies, particularly iron deficiency, a major contributor to anemia [64, 65]. Meng et al. reported that higher dietary diversity scores, particularly for fruits, vegetables, and animal products, were positively correlated with adequate micronutrient intake especially iron and Net Absorption Rate (NAR) for a range of micronutrients in their study population [66]. The rationale behind a diet with higher dietary diversity score is clear. Plant-based sources like fruits and vegetables offer a rich tapestry of vitamins and minerals, including carotene, potassium, vitamin C, the B vitamin family, and even some vitamin D. Animal foods complement this profile by providing essential minerals like magnesium, zinc, and iron, alongside calcium and vitamins E and B complex vitamins [66,67,68]. Also, Faber et al. [67] observed that while over 85% of children in South Africa consistently consumed cereals and root vegetables, a staple food group, less than a quarter achieved the Minimum Dietary Diversity (MDD) standard [69]. This implies that frequent cereal and root vegetable intake might not guarantee adequate micronutrients. The low MDD scores likely indicate an infrequent intake of fruits, vegetables, and animal-based foods, which are crucial sources of micronutrients [69]. A diverse diet rich in iron-rich foods like red meat, poultry, fish, legumes, and leafy green vegetables is crucial for maintaining adequate iron stores and associated with anemia development [70, 71]. Children and adolescents with limited dietary diversity are more likely to have lower intakes of these essential iron sources, increasing their vulnerability to anemia [30, 72].

Iron, an essential micronutrient, plays a crucial role in oxygen transport, energy metabolism, and immune function. Dietary iron exists in two primary forms: heme iron and non-heme iron, which differ in bioavailability and dietary sources. Heme iron, found predominantly in animal-based foods such as red meat, poultry, and fish, has a higher absorption rate (15–35%) due to its direct uptake by enterocytes via specific transport mechanisms [73]. In contrast, non-heme iron, primarily sourced from plant-based foods such as legumes, whole grains, nuts, and leafy green vegetables, has a lower absorption rate (2–20%) and is influenced by dietary enhancers (e.g., vitamin C) and inhibitors (e.g., phytates, polyphenols, and calcium) [74].

Dietary diversity plays a critical role in ensuring adequate iron intake and optimizing iron bioavailability. A diverse diet that includes both heme and non-heme iron sources enhances total iron absorption and reduces the odds of iron deficiency anemia. Populations with limited dietary diversity, particularly those relying heavily on plant-based diets without sufficient enhancers of non-heme iron absorption, may face an increased odds of anemia. For instance, individuals consuming predominantly cereal-based diets with minimal access to vitamin C-rich fruits or animal products are more susceptible to iron deficiency anemia due to the inhibitory effects of phytates and polyphenols [75]. Conversely, those with higher dietary diversity, incorporating both animal and plant-based sources, tend to have improved iron status due to better iron bioavailability [76].

The findings from our meta-analysis highlight the association between dietary diversity and anemia odds in children and adolescents. Given the distinct absorption characteristics of heme and non-heme iron, future research should explore strategies to improve dietary diversity by incorporating iron-rich food sources alongside dietary enhancers. Public health initiatives should emphasize the consumption of a balanced diet that includes heme iron sources where feasible and non-heme iron sources with enhancers such as vitamin C to maximize iron absorption and mitigate odds of anemia [77].

Our findings also add to the growing body of evidence suggesting the importance of promoting dietary diversity across different age groups. Previous studies have primarily focused on children under 5 years old, a critical period for growth and development. However, our analysis for adolescents (aged 6–18 years) also revealed a significant association between low dietary diversity and anemia. This highlights the need for continued emphasis on maintaining a healthy and diverse diet throughout childhood and adolescence to ensure optimal iron intake and reduce the likelihood of anemia.

Prespecified subgroups included age categories (0–5 years, 6–18 years) and study design (cross-sectional, case–control), as these factors were identified as potential effect modifiers based on prior literature. Post-hoc analyses were conducted based on geographic location and dietary diversity assessment methods to further investigate sources of heterogeneity. Despite these efforts, significant heterogeneity remained, suggesting that unmeasured confounding factors such as socioeconomic status, dietary patterns, and healthcare access may contribute to variability in the results. Future research should consider more granular subgroup analyses and standardized dietary diversity assessment tools to reduce heterogeneity.

Confounding factors such as child’s age, sex, maternal education, and household socioeconomic status were frequently controlled for across studies. These factors are known to influence outcomes like anemia, and accounting for them likely strengthened the reliability of the findings in many studies. Studies that adjusted for a broader range of confounders (e.g., maternal nutrition, child’s feeding practices, household food insecurity) generally reported more consistent and stronger associations between dietary patterns and anemia. For instance, studies that included variables such as iron intake and maternal health indicators (e.g., mother's smoking, number of under-five children, stunting, and wasting) provided a more nuanced understanding of the relationship between dietary factors and anemia in children. However, several studies did not adjust for key confounders, such as socioeconomic status, maternal education, or dietary intake. In particular, three studies [33, 34, 39] did not make adjustments for any potential confounders, which limits the ability to draw definitive conclusions from these studies. The lack of adjustment for these factors could have introduced bias, potentially overstating or understating the true relationship between dietary patterns and anemia. This highlights the importance of controlling for confounders in future research, particularly those related to socioeconomic status and nutrition, which are known to significantly influence anemia prevalence.

Recognizing the potential for nutritional deficiencies during early life stages to persist into later stages, the WHO advocates for improved micronutrient intake. Their recommendations include consuming nutrient-dense foods, such as animal products or fortified options, to address potential shortfalls [63, 78]. While our study does not directly elucidate the underlying mechanisms, several plausible pathways warrant consideration. First, dietary diversity ensures a broader array of micronutrients, which collectively contribute to hemoglobin synthesis. Iron, in particular, plays a central role in erythropoiesis, and its deficiency remains a primary driver of anemia. Second, dietary variety may serve as a proxy for overall nutritional status and food security. Individuals with diverse diets are more likely to meet their nutritional needs, enhancing iron absorption and utilization. Future investigations should explore these mechanistic links to deepen our understanding of the observed association.

Based on our knowledge, the present study was the first systematic review and meta-analysis study which evaluated the association between dietary diversity and odds of anemia among children and adolescents. Despite the comprehensive approach and robust findings, this systematic review and meta-analysis have several limitations that warrant discussion. First, the observational nature of the included studies limits the ability to infer causality. While the associations observed between dietary diversity and anemia are significant, we cannot definitively establish that low dietary diversity causes anemia. Second, given the high odds of bias in many included studies, particularly in confounding and dietary exposure measurement, the findings should be interpreted with caution. Future research should employ longitudinal or experimental designs to strengthen causal inference. Third, there was considerable heterogeneity among the studies included in our analysis. This heterogeneity could stem from variations in study populations, settings, dietary diversity assessment methods, and criteria for defining anemia. Although we performed subgroup analyses based on continent, type of anemia, sex, and odds of bias, these factors did not fully explain the observed heterogeneity. This suggests that other unmeasured variables may contribute to the variability in results. Standardizing the measurement tools and criteria across studies could enhance comparability and reduce heterogeneity in future research. Finally, the assessment of dietary diversity was based on various tools and indices across the included studies, which may impact the consistency of the findings. Different studies used different criteria and cut-off points to evaluate dietary diversity, which might affect the comparability and generalizability of the results. Additionally, dietary diversity scores do not capture the quality or nutrient content of the diet, which are crucial factors influencing anemia. Future research should aim to standardize dietary diversity assessment methods and incorporate more detailed dietary quality measurements.

Conclusion

In this systematic review and meta-analysis, we examined the association between dietary diversity and the odds of anemia in children and adolescents. Based on current evidence, there is low certainty that inadequate dietary diversity is associated with increased odds of anemia in children and adolescents. While the findings suggest a potential link, limitations in the underlying studies including high odds of bias, inconsistency, and imprecision, warrant cautious interpretation. Public health interventions to improve dietary diversity may be beneficial, but further high-quality research is needed to confirm these associations. Future research should aim to standardize dietary diversity assessment methods and incorporate detailed dietary quality measurements to enhance comparability and reduce heterogeneity.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Seifu BL, Tesema GA. Individual-and community-level factors associated with anemia among children aged 6–23 months in sub-Saharan Africa: evidence from 32 sub-Saharan African countries. Archives of Public Health. 2022;80(1):183.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Tulchinsky TH. Micronutrient deficiency conditions: global health issues. Public Health Rev. 2010;32:243–55.

    Article  Google Scholar 

  3. Shimano KA, Narla A, Rose MJ, Gloude NJ, Allen SW, Bergstrom K, et al. Diagnostic work-up for severe aplastic anemia in children: consensus of the North American Pediatric Aplastic Anemia Consortium. Am J Hematol. 2021;96(11):1491–504.

    Article  PubMed  Google Scholar 

  4. Dutta M, Bhise M, Prashad L, Chaurasia H, Debnath P. Prevalence and risk factors of anemia among children 6–59 months in India: A multilevel analysis. Clinical Epidemiology and Global Health. 2020;8(3):868–78.

    Article  Google Scholar 

  5. Habib A, Kureishy S, Soofi S, Hussain I, Rizvi A, Ahmed I, et al. Prevalence and Risk Factors for Iron Deficiency Anemia among Children under Five and Women of Reproductive Age in Pakistan: Findings from the National Nutrition Survey 2018. Nutrients. 2023;15(15):3361.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Assaf S, Juan C. Stunting and anemia in children from urban poor environments in 28 low and middle-income countries: a meta-analysis of demographic and health survey data. Nutrients. 2020;12(11):3539.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sunardi D, Bardosono S, Basrowi RW, Wasito E, Vandenplas Y. Dietary determinants of anemia in children aged 6–36 months: A cross-sectional study in Indonesia. Nutrients. 2021;13(7):2397.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rafati A, Pasebani Y, Jameie M, Yang Y, Jameie M, Ilkhani S, et al. Association of SARS-CoV-2 Vaccination or Infection With Bell Palsy: A Systematic Review and Meta-analysis. JAMA Otolaryngol Head Neck Surg. 2023;149(6):493–504.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Mantadakis E, Chatzimichael E, Zikidou P. Iron deficiency anemia in children residing in high and low-income countries: risk factors, prevention, diagnosis and therapy. Mediterr J Hematol Infect Dis. 2020;12(1):47–59.

  10. Orsango AZ, Habtu W, Lejisa T, Loha E, Lindtjørn B, Engebretsen IMS. Iron deficiency anemia among children aged 2–5 years in southern Ethiopia: a community-based cross-sectional study. PeerJ. 2021;9:e11649.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Chandra J, Dewan P, Kumar P, Mahajan A, Singh P, Dhingra B, et al. Diagnosis, treatment and prevention of nutritional anemia in children: recommendations of the joint committee of pediatric hematology-oncology chapter and pediatric and adolescent nutrition society of the Indian Academy of Pediatrics. Indian Pediatr. 2022;59(10):782–801.

    Article  PubMed  Google Scholar 

  12. Karimi E, Gholizadeh M, Abdolahi M, Sedighiyan M, Salehinia F, Siri G, et al. Effect of vitamin B1 supplementation on blood creatinine and lactate levels and clinical outcomes in patients in intensive care units: a systematic review and meta-analysis of randomized controlled trials. Nutr Rev. 2024;82(6):804–14.

    Article  PubMed  Google Scholar 

  13. Gandomkar H, Mahmoodzadeh H, Tavakoli H, Fazeli M, Rezaei J. Garlic-induced esophageal perforation: A case series. Asian J Surg. 2020;43(6):696–7.

    Article  PubMed  Google Scholar 

  14. Organization WH. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. World Health Organization; 2011.

  15. Joo EY, Kim KY, Kim DH, Lee J-E, Kim SK. Iron deficiency anemia in infants and toddlers. Blood research. 2016;51(4):268.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lozoff B, Kaciroti N, Walter T. Iron deficiency in infancy: applying a physiologic framework for prediction. Am J Clin Nutr. 2006;84(6):1412–21.

    Article  CAS  PubMed  Google Scholar 

  17. Lemoine A, Tounian P. Childhood anemia and iron deficiency in sub-Saharan Africa–risk factors and prevention: A review. Arch Pediatr. 2020;27(8):490–6.

    Article  CAS  PubMed  Google Scholar 

  18. Lozoff B, Beard J, Connor J, Felt B, Georgieff M, Schallert T. Long-lasting neural and behavioral effects of iron deficiency in infancy. Nutr rev. 2006;64(2):S34–43.

    Article  PubMed  Google Scholar 

  19. Moschovis PP, Wiens MO, Arlington L, Antsygina O, Hayden D, Dzik W, et al. Individual, maternal and household risk factors for anaemia among young children in sub-Saharan Africa: a cross-sectional study. BMJ Open. 2018;8(5): e019654.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Chen S, Shimpuku Y, Honda T, Mwakawanga DL, Mwilike B. Dietary diversity moderates household economic inequalities in the double burden of malnutrition in Tanzania. Public Health Nutr. 2024:8(4):1–24.

  21. Ch LD, Bharath Y, Bliznashka L, Kumar TV, Jonnala V, Chekka V, et al. Evidence of potential impacts of a nutrition-sensitive agroecology program in Andhra Pradesh, India, on dietary diversity, nutritional status, and child development. PLoS ONE. 2024;19(5): e0286356.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Atosona A, Mohammed JA, Issahaku H, Saani K, Addae HY, Azupogo F. Maternal employment status and child age are positive determinants of minimum dietary diversity among children aged 6–23 months in Sagnarigu municipality, Ghana: a cross-sectional study. BMC Nutr. 2024;10(1):57.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hasanloei MAV, Rahimlou M, Eivazloo A, Sane S, Ayremlou P, Hashemi R. Effect of Oral Versus Intramuscular Vitamin D Replacement on Oxidative Stress and Outcomes in Traumatic Mechanical Ventilated Patients Admitted to Intensive Care Unit. Nutr Clin Pract. 2020;35(3):548–58.

    Article  CAS  PubMed  Google Scholar 

  24. Visser M, Van Zyl T, Hanekom SM, Baumgartner J, Van Der Hoeven M, Taljaard-Krugell C, et al. Associations of dietary diversity with anaemia and iron status among 5-to 12-year-old schoolchildren in South Africa. Public Health Nutr. 2021;24(9):2554–62.

    Article  PubMed  Google Scholar 

  25. Abdollahi H, Salehinia F, Badeli M, Karimi E, Gandomkar H, Asadollahi A, et al. The Biochemical Parameters and Vitamin D Levels in ICU Patients with Covid-19: A Cross-Sectional Study. Endocr Metab Immune Disord Drug Targets. 2021;21(12):2191–202.

    Article  CAS  PubMed  Google Scholar 

  26. Agustina R, Nadiya K, Andini EA, Setianingsih AA, Sadariskar AA, Prafiantini E, et al. Associations of meal patterning, dietary quality and diversity with anemia and overweight-obesity among Indonesian school-going adolescent girls in West Java. PLoS ONE. 2020;15(4): e0231519.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Alamneh YM, Akalu TY, Shiferaw AA, Atnaf A. Magnitude of anemia and associated factors among children aged 6–59 months at Debre Markos referral hospital, Northwest Ethiopia: a hospital-based cross-sectional study. Ital J Pediatr. 2021;47(1):172.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Alemayehu M, Meskele M, Alemayehu B, Yakob B. Prevalence and correlates of anemia among children aged 6–23 months in Wolaita Zone, Southern Ethiopia. PLoS ONE. 2019;14(3): e0206268.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Fage SG, Egata G, Dessie Y, Kumsa FA, Mizana BA. Anemia among School Adolescents in Haramaya Town, Eastern Ethiopia: Cross-Sectional Study. Nutrition and metabolic insights. 2020;13:1178638820953131.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Fentaw W, Belachew T, Andargie A. Anemia and associated factors among 6 to 59 months age children attending health facilities in Kombolcha town, Northeast Ethiopia: a facility-based cross-sectional study. BMC Pediatr. 2023;23(1):209.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Fentie K, Wakayo T, Gizaw G. Prevalence of Anemia and Associated Factors among Secondary School Adolescent Girls in Jimma Town, Oromia Regional State. Southwest Ethiopia Anemia. 2020;2020:5043646.

    PubMed  Google Scholar 

  32. Getaneh Z, Enawgaw B, Engidaye G, Seyoum M, Berhane M, Abebe Z, et al. Prevalence of anemia and associated factors among school children in Gondar town public primary schools, northwest Ethiopia: A school-based cross-sectional study. PLoS ONE. 2017;12(12): e0190151.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Gonete KA, Tariku A, Wami SD, Derso T. Prevalence and associated factors of anemia among adolescent girls attending high schools in Dembia District, Northwest Ethiopia, 2017. Archives of public health = Archives belges de sante publique. 2018;76:79.

  34. Gosdin L, Martorell R, Bartolini RM, Mehta R, Srikantiah S, Young MF. The co-occurrence of anaemia and stunting in young children. Matern Child Nutr. 2018;14(3):78–93.

  35. Hiruy AF, Teshome AA, Desta YT, Zuo XZ, He SQ, Assefa EG, Ying CJ. Dietary condition and feeding practices of children aged 6–23 months in Ethiopia: analysis of 2005–2016 demographic and health survey. Eur J Clin Nutr. 2021;75(7):1047–59.

    Article  CAS  PubMed  Google Scholar 

  36. Kathuria N, Bandyopadhyay P, Srivastava S, Garg PR, Devi KS, Kurian K, et al. Association of minimum dietary diversity with anaemia among 6–59 months’ children from rural India: An evidence from a cross-sectional study. Journal of Family Medicine and Primary Care. 2023;12(2):313–9.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Khanal A, Paudel R, Wagle CN, Subedee S, Pradhan PMS. Prevalence of anemia and its associated factors among adolescent girls on Weekly Iron Folic Acid supplementation (WIFAS) implemented and non-implemented schools at Tokha municipality, Kathmandu. PLOS global public health. 2024;4(1): e0002515.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kumar M, Mohanty PC. Undernutrition and anaemia among Indian adolescents: role of dietary diversity and hygiene practices. J Nutr Sci. 2023;12:33–45.

  39. Lweno O, Hertzmark E, Darling AM, Noor R, Bakari L, Sudfeld C, et al. The High Burden and Predictors of Anemia Among Infants Aged 6 to 12 Months in Dar es Salaam. Tanzania Food and nutrition bulletin. 2022;43(1):68–83.

    Article  PubMed  Google Scholar 

  40. Malako BG, Asamoah BO, Tadesse M, Hussen R, Gebre MT. Stunting and anemia among children 6–23 months old in Damot Sore district. Southern Ethiopia BMC nutrition. 2019;5:3.

    PubMed  Google Scholar 

  41. Malako BG, Teshome MS, Belachew T. Anemia and associated factors among children aged 6–23 months in Damot Sore District, Wolaita Zone. South Ethiopia BMC hematology. 2018;18:14.

    Article  PubMed  Google Scholar 

  42. Mank I, De Neve JW, Mauti J, Gyengani GA, Somé PA, Shinde S, et al. Prevalence of Obesity and Anemia Among Early Adolescents in Junior Secondary Schools: A Cross-Sectional Study in Ouagadougou, Burkina Faso. J Sch Health. 2022;92(11):1081–95.

    Article  PubMed  Google Scholar 

  43. Molla A, Egata G, Mesfin F, Arega M, Getacher L. Prevalence of Anemia and Associated Factors among Infants and Young Children Aged 6–23 Months in Debre Berhan Town, North Shewa. Ethiopia Journal of nutrition and metabolism. 2020;2020:2956129.

    PubMed  Google Scholar 

  44. Nair KM, Fernandez-Rao S, Nagalla B, Kankipati RV, Punjal R, Augustine LF, et al. Characterisation of anaemia and associated factors among infants and pre-schoolers from rural India. 2016;19(5):861–71.

    Google Scholar 

  45. Parbey PA, Tarkang E, Manu E, Amu H, Ayanore MA, Aku FY, et al. Risk Factors of Anaemia among Children under Five Years in the Hohoe Municipality, Ghana: A Case Control Study. Anemia. 2019;10(5):52–70.

  46. Said FA, Khamis AG, Habib A, Yang H, He Z, Luo X. Prevalence and Determinants of Anemia among Children in Zanzibar, Tanzania: Analysis of Cross-Sectional Population Representative Surveys. Children (Basel, Switzerland). 2021;8(12):143–66.

  47. Sodde FM, Liga AD, Jabir YN, Tamiru D, Kidane R. Magnitude and predictors of anemia among preschool children (36–59 months) in Atingo town, Jimma, Ethiopia. Health science reports. 2023;6(6): e1358.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Tegegne M, Abate KH, Belachew T. Anaemia and associated factors among children aged 6–23 months in agrarian community of Bale zone: a cross-sectional study. J Nutr Sci. 2022;11:154–73.

  49. Tiku YS, Mekonnen TC, Workie SB, Amare E. Does Anaemia Have Major Public Health Importance in Children Aged 6–59 Months in the Duggina Fanigo District of Wolaita Zone, Southern Ethiopia? Ann Nutr Metab. 2018;72(1):3–11.

    Article  PubMed  Google Scholar 

  50. Tura MR, Egata G, Fage SG, Roba KT. Prevalence of Anemia and Its Associated Factors Among Female Adolescents in Ambo Town, West Shewa. Ethiopia Journal of blood medicine. 2020;11:279–87.

    Article  PubMed  Google Scholar 

  51. Woldie H, Kebede Y, Tariku A. Factors Associated with Anemia among Children Aged 6–23 Months Attending Growth Monitoring at Tsitsika Health Center, Wag-Himra Zone, Northeast Ethiopia. Journal of nutrition and metabolism. 2015;2015: 928632.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Zavala E, Adler S, Wabyona E, Ahimbisibwe M, Doocy S. Trends and determinants of anemia in children 6–59 months and women of reproductive age in Chad from 2016 to 2021. BMC nutrition. 2023;9(1):117.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, Schünemann HJJB. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. 2008;336(7650):924–6.

  54. Stang AJEjoe. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. 2010;25:603–5.

  55. Allali S, Brousse V, Sacri A-S, Chalumeau M, de Montalembert M. Anemia in children: prevalence, causes, diagnostic work-up, and long-term consequences. Expert Rev Hematol. 2017;10(11):1023–8.

    Article  CAS  PubMed  Google Scholar 

  56. Shibeshi AH, Mare KU, Kase BF, Wubshet BZ, Tebeje TM, Asgedom YS, et al. The effect of dietary diversity on anemia levels among children 6–23 months in sub-Saharan Africa: A multilevel ordinal logistic regression model. PLoS ONE. 2024;19(5): e0298647.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Darapheak C, Takano T, Kizuki M, Nakamura K, Seino K. Consumption of animal source foods and dietary diversity reduce stunting in children in Cambodia. International archives of medicine. 2013;6(1):1–11.

    Article  Google Scholar 

  58. Rah JH, Akhter N, Semba RD, De Pee S, Bloem MW, Campbell AA, et al. Low dietary diversity is a predictor of child stunting in rural Bangladesh. Eur J Clin Nutr. 2010;64(12):1393–8.

    Article  CAS  PubMed  Google Scholar 

  59. Disha A, Purnima M, Rahul R. Household food insecurity is associated with higher child undernutrition in Bangladesh, Ethiopia, and Vietnam, but the effect is not mediated by child dietary diversity. J Nutr. 2013;143(12):2015–21.

    Article  Google Scholar 

  60. Laster LER, Lovelady CA, West DG, Wiltheiss GA, Brouwer RJ, Stroo M, Østbye T. Diet quality of overweight and obese mothers and their preschool children. J Acad Nutr Diet. 2013;113(11):1476–83.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Fox MK, Pac S, Devaney B, Jankowski L. Feeding infants and toddlers study: what foods are infants and toddlers eating? J Am Diet Assoc. 2004;104:22–30.

    Article  Google Scholar 

  62. Kim SA, Moore LV, Galuska D, Wright AP, Harris D, Grummer-Strawn LM, et al. Vital signs: fruit and vegetable intake among children—United States, 2003–2010. MMWR Morb Mortal Wkly Rep. 2014;63(31):671–6.

    PubMed  PubMed Central  Google Scholar 

  63. Molani-Gol R, Kheirouri S, Alizadeh M. Does the high dietary diversity score predict dietary micronutrients adequacy in children under 5 years old? A systematic review. J Health Popul Nutr. 2023;42(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Heidari-Beni M, Riahi R, Massoudi S, Qorbani M, Kelishadi R. Association between dietary diversity score and anthropometric indices among children and adolescents: the weight disorders survey in the CASPIAN-IV study. J Sci Food Agric. 2021;101(12):5075–81.

    Article  CAS  PubMed  Google Scholar 

  65. Liu T, Broverman S, Puffer ES, Zaltz DA, Thorne-Lyman AL, Benjamin-Neelon SE. Dietary Diversity and Dietary Patterns in School-Aged Children in Western Kenya: A Latent Class Analysis. Int J Environ Res Public Health. 2022;19(15):123–43.

  66. Meng L, Wang Y, Li T, Loo-Bouwman CAv, Zhang Y, Man-Yau Szeto I. Dietary diversity and food variety in Chinese children aged 3–17 years: are they negatively associated with dietary micronutrient inadequacy? Nutrients. 2018;10(11):1674.

  67. Yang J, editor Design tailored nutrition and weight control recommendations using nutrigenetics and FFQ. Health Information Science: 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020, Proceedings 9; 2020: Springer.

  68. Control CCfD, Prevention. Chinese food composition table. Peking University Medical Press Beijing, China; 2009.

  69. Faber M, Laubscher R, Berti C. Poor dietary diversity and low nutrient density of the complementary diet for 6-to 24-month-old children in urban and rural K wa Z ulu-N atal, S outh A frica. Matern Child Nutr. 2016;12(3):528–45.

    Article  PubMed  Google Scholar 

  70. Minja EG, Mrimi EC, Mponzi WP, Mollel GJ, Lang C, Beckmann J, et al. Prevalence and Determinants of Undernutrition in Schoolchildren in the Kilombero District, South-Eastern Tanzania. Trop Med Infect Dis. 2024;9(5):97–116.

  71. Abdollahi H, Tavakoli H, Mojtahedi Y, Sedighiyan M, Abdolahi M, Jamshidi MS, et al. Evaluation of Depression, Anxiety and Stress Scores in Patients with Covid-19: A Cross-Sectional Study. Archives of Anesthesia and Critical Care. 2024.

  72. Pradeilles R, Landais E, Pareja R, Eymard-Duvernay S, Markey O, Holdsworth M, et al. Exploring the magnitude and drivers of the double burden of malnutrition at maternal and dyad levels in peri-urban Peru: A cross-sectional study of low-income mothers, infants and young children. Matern Child Nutr. 2023;19(4): e13549.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Gallo Ruelas M, Alvarado-Gamarra G, Aramburu A, Dolores-Maldonado G, Cueva K, Rojas-Limache G, et al. A comparative analysis of heme vs non-heme iron administration: a systematic review and meta-analysis of randomized controlled trials. Eur J Nutr. 2025;64(1):51.

    Article  Google Scholar 

  74. Aglago EK, Cross AJ, Riboli E, Fedirko V, Hughes DJ, Fournier A, et al. Dietary intake of total, heme and non-heme iron and the risk of colorectal cancer in a European prospective cohort study. Br J Cancer. 2023;128(8):1529–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Moscheo C, Licciardello M, Samperi P, La Spina M, Di Cataldo A, Russo G. New insights into iron deficiency anemia in children: a practical review. Metabolites. 2022;12(4):289.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Animasahun BA, Itiola AY. Iron deficiency and iron deficiency anaemia in children: physiology, epidemiology, aetiology, clinical effects, laboratory diagnosis and treatment: literature review. Journal of xiangya medicine. 2021;6.

  77. Basrowi RW, Dilantika C. Optimizing iron adequacy and absorption to prevent iron deficiency anemia: the role of combination of fortified iron and vitamin C. World Nutrition Journal. 2021;5(S1):33–9.

    Article  Google Scholar 

  78. Ndungu Z, Chege P. Dietary diversity and micronutrients adequacy of diets consumed by school aged children in Nairobi City County, Kenya. Nutr Food Technol Open Access. 2019;5(1):10.16966.

Download references

Acknowledgements

Not applicable.

Clinical trial number

Not applicable.

Funding

Non.

Author information

Authors and Affiliations

  1. Department of Physical Education, Henan University of Animal Husbandry and Economy, No.6, Longzihu North Road, Zhengdong New District, Zhengzhou, Henan Province, 450000, China

    Haiting Li

  2. Department of Community Nutrition, Vice-Chancellery for Health, Shiraz University of Medical Sciences, Shiraz, Iran

    Seyedeh Parisa Moosavian

  3. Assistant Professor of General Pediatrician, Emergency division, Bahrami hospital, school of medicine, Tehran University of Medical Sciences, Tehran, Iran

    Niloufar Ghanbari

  4. Pediatric Respiratory Disease and Sleep Medicine Research Center, Tehran University of Medical Sciences, Tehran, Iran

    Seyed Hossein Mirlohi

  5. Metabolic Diseases Research Center, Health and Metabolic Research Institute, Zanjan University of Medical Sciences, Zanjan, Iran

    Mehran Rahimlou

Authors
  1. Haiting Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Seyedeh Parisa Moosavian
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Niloufar Ghanbari
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Seyed Hossein Mirlohi
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Mehran Rahimlou
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

H.L and S.P.M. designed this study. H.L and S.P.M. conducted the literature search. N.G and S.H.M contributed in data extraction. M.R performed the statistical analysis and interpretation of the data. SPM, MR, and H.L wrote the manuscript. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Mehran Rahimlou.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Moosavian, S.P., Ghanbari, N. et al. Association of dietary diversity and odds of anemia in children and adolescents: a systematic review and meta-analysis of observational studies. BMC Nutr 11, 83 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40795-025-01069-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40795-025-01069-3

Keywords

BMC Nutrition

ISSN: 2055-0928

Contact us