Effect of Increasing Red Meat Intake on Iron Status in Adults with Normal and Suboptimal Iron Status: A Systematic Literature Review and Meta-Analysis of Intervention Studies

Abstract

Context

Iron deficiency is the most prevalent nutrient deficiency globally. There is increasing interest in the use of food-based approaches for improving and maintaining iron status.

Objective

A systematic literature review was conducted to identify, critically-appraise, and meta-analyze data from intervention studies that investigated the effect of increasing red meat intake on iron status in adults.

Data Extraction

The search was conducted using the PubMed, Web of Science, Scopus, Embase, Cochrane, SPORTDiscus, and Google Scholar databases, as well as other supplementary search strategies up to October 2024. Inclusion criteria for reviewed articles were participants who were adults aged 18 to 70 years, interventions that involved ≥4 weeks of increased red meat intake, included a suitable control condition, and measured at least 1 biomarker of iron status. The initial search yielded 5212 articles, with 10 articles (n = 397 participants) meeting the inclusion criteria.

Data Analysis

The effect of intervention on markers of iron status were analyzed using a meta-analytic multivariate model, and the potential moderating effect of multiple variables were considered. The final meta-analysis included 42 effects (k) for serum ferritin (k = 25) and hemoglobin (Hb; k = 17) concentrations. Interventions involving increasing red meat intake had no significant effect on serum ferritin concentrations (raw mean change difference [RMCD] = 1.87 μg L^–1; 95% CI, –0.73 to 4.48; t = 1.619; P = .139) but did have a positive effect on Hb concentrations (RMCD = 2.36 g L^–1; 95% CI, 0.71 to 4.02; t = 3.297; P = .011). Moderator analysis revealed a positive effect of increasing red meat intake on serum ferritin concentrations when the intervention lasted at least 8 weeks (RMCD = 2.27 μg L^–1; 95% CI, 0.87 to 3.67), and longer than 16 weeks (RMCD = 5.62 μg L^–1; 95% CI, 0.67 to 10.6).

Conclusions

Increasing red meat intake can improve iron status as indicated by increases in serum ferritin and Hb concentrations, but the extent to which such increases are clinically meaningful remains to be established.

Systematic Review Registration

PROSPERO registration No. CRD42023479349.

INTRODUCTION

Iron deficiency (ID) is the most prevalent nutrient deficiency globally, with approximately 25% of the world’s population living with inadequate iron stores.^1,2 The high prevalence of ID is a concern because iron in the human body is essential for many important physiological functions, including oxygen transport, energy production, and neurotransmitter synthesis.^3–5 Inadequate iron stores have been linked to various negative health outcomes, such as increased subjective reporting of fatigue, impaired cognitive function, diminished productivity, and a reduced capacity for physical activity.^6–8 Furthermore, ID is the leading diet-related cause of anemia, which, when severe, can lead to lethargy, breathlessness, impaired growth and development, and adverse pregnancy outcomes.^9–11 Factors that increase the risk of ID include consumption of a diet low in bioavailable iron, participation in high volumes of long duration aerobic exercise, heavy menstrual bleeding, and reduced iron absorption.^7,10,12 As such, ID, with and without anemia, is common among specific population groups such as athletes, women of childbearing age, and young children.^1,2

Oral iron supplementation is the most common strategy for the prevention and treatment of ID, with less attention to date being given to dietary approaches.¹³ Although the effectiveness of oral iron supplementation is well documented,^13–16 side effects can include nausea, vomiting, constipation, and diarrhea, which may lead to the discontinuation of treatment and thereby reduce its overall effectiveness. Alternatively, increasing iron intake from dietary sources can improve iron status.^11,17 There are 2 types of dietary iron: heme iron present in foods from animal flesh, and nonheme iron present in foods of both animal and plant origins.¹⁸ Heme iron is most bioavailable and is estimated to contribute to ≥40% of total absorbed iron, making it beneficial for the prevention and treatment of ID.¹⁹ In contrast, the bioavailability of nonheme iron is low and is affected by the presence of other components of the diet that can either inhibit (eg, phytate, polyphenols, calcium) or enhance (eg, ascorbic acid, fortification iron) iron absorption.^20–22

Red meat, such as beef, pork, and lamb, is high in dietary iron (∼2.8 mg 100 g^–1)²³ and provides a source of both heme (∼40%) and nonheme (∼60%) iron.^11,19 Red meat also contains an active compound known as “meat factor,” which promotes greater absorption of nonheme iron.^20,24,25 Although the precise mechanism by which the meat factor enhances iron absorption is still not fully understood, this absorption-enhancing phenomenon has been demonstrated with beef and pork.^26,27 Previous observational and intervention studies, although limited in number, support the benefit of increased red meat intake in maintaining higher iron status in various population groups.^11,28,29 For example, a meta-analysis of 24 cross-sectional studies found that individuals who consumed red meat had greater iron stores than did their vegetarian counterparts, with a mean difference in serum ferritin concentration of almost 30 µg L^–1 between the 2 groups.²⁸ Similarly in an intervention study, 52 weeks of increased red meat intake improved serum ferritin concentrations by 13 µg L^–1 in women of childbearing age.³⁰

Previous systematic reviews in this area have largely focused on cross-sectional and observational studies of how dietary intake of red meat influences iron status.^11,31 Identifying whether increasing red meat intake as a dietary intervention results in improved iron status is a topical and important question, especially considering the recent recommendations to reduce red meat consumption due to health, environmental, and ethical concerns associated with its production and consumption.^19,32 Thus, our purpose for conducting this systematic literature review is to identify, critically appraise, and meta-analyze data from intervention studies that investigated the effects of a period of increased red meat intake on iron status in adults with normal and suboptimal iron status.

METHODS

Literature Search and Management

All elements of this protocol align with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols Statement (PRISMA-P) (Figure S1).³³ Consistent with PRISMA guidelines,^33,34 the review protocol was registered with the PROSPERO database (registration no. CRD42023479349) on December 5, 2023. The following databases were searched from their inception until October 14, 2024, to obtain the literature used in this review: PubMed, Web of Science, Embase, SPORTDiscus, Cochrane, Scopus, and Google Scholar (the first 500 hits were evaluated). The first author (L.M.) gathered the relevant articles using the following search string for all databases: (“red meat” OR meat) AND (intervention OR supplement* OR administration OR diet OR consumption) AND (“iron status” OR ferritin OR hemoglobin OR “iron stores” OR iron). Results were limited to articles published in the English language that included only human participants.

All duplicate results were removed, and the remaining articles were screened using the Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). Two independent reviewers (L.M., V.S.F., K.V., or B.E.) assessed each article for eligibility by applying the exclusion and inclusion criteria to the titles and abstracts. Each study carried forward from this stage was fully read and assessed independently by 2 authors (L.M., V.S.F., K.V., or B.E.) for final inclusion. Conflicting opinions between authors were resolved via discourse, with a third author acting as a mediator, if necessary. Reasons for inclusion and exclusion of studies were recorded and are listed in Figure 1. After this database search and selection of the preliminary list of included studies, the reference lists of all included studies and relevant review articles were also screened. A backward search using the Cited by and Related articles tabs in Google Scholar was also conducted for all included studies. Furthermore, the corresponding authors of included studies were contacted via email or ResearchGate, up to 3 times, with a request for unpublished or undiscovered research.

Figure 1.

Preferred Reporting Items for Systematic Review and Meta-Analysis flow diagram. Detailed flow of studies examined from the initial search to the final inclusion.

Study Selection

Serum ferritin concentration is used as the principal biomarker of iron status in humans because it is the major iron storage protein and generally reflective of total body iron stores.³⁵ Hemoglobin (Hb) is also considered a functional biomarker of iron status, along with serum iron, soluble transferrin (Tf), Tf saturation, hematocrit, and total iron-binding capacity (TIBC).¹⁷ Articles were considered eligible if the following inclusion criteria were fulfilled: (1) were peer-reviewed and reported in English; (2) were experimental in design; (3) involved an intervention of increased red meat intake that lasted at least 4 weeks; (4) measured at least 1 blood biomarker of iron status (eg, ferritin, Hb, Tf saturation); (5) used at least 2 data points (before and after measures); (6) included adults with an average age of ≥18 to ≤70 years; and (7) involved a control group that either consumed their habitual diet or a prescribed diet that did not involve an increase in red meat intake. The exclusion criteria were as follows: (1) observational (cohort or case-control) study designs; (2) included adults who had existing cardiovascular or metabolic conditions, were pregnant, or were undergoing clinical treatment; and (3) used red meat in conjunction with other, confounding nutrition interventions (Table 1).

Table 1.

PICOS criteria for inclusion and exclusion of studies.

Component	Inclusion	Exclusion
Population	Adults aged 18 to 70 y	Adults with chronic illness, metabolic conditions, or pregnancy
Intervention	At least 4 wk of increased red meat intake	Red meat in conjunction with other confounding interventions Insufficient details provided about the intervention
Comparator	Consumption of habitual diet or a prescribed diet not rich in red meat	Insufficient details provided about the control condition provided
Outcomes	At least 1 biomarker of iron status was measured before and after the intervention
Study design	Intervention studies (RCT, CT)	Observational studies

Abbreviations: CT, controlled trial; PICOS: Population, Intervention, Comparator, Outcomes, and Study design; RCT, randomized controlled trial.

Data Extraction, Moderators, and Risk of Bias

We autonomously extracted sample sizes, means, and SD or SEs for the outcome measures from each study. To ensure the reliability of the data used in effect size calculation, inter-rater agreement was evaluated using an unweighted Cohen’s κ for categorical data and an intraclass correlation coefficient for continuous data. Any disparities were identified and resolved before finalizing the calculations.

In addition to quantitative data, predetermined moderators such as characteristics of experimental interventions (duration, red meat type and quantity) and control conditions (type), participant demographics (average age, sex, adherence to intervention, health status), and article attributes (country, publication status, publication year) were also extracted. Inter-rater agreement for all coded moderators was evaluated using an unweighted Cohen’s κ. Any discrepancies were identified and resolved before concluding the calculations.

Risk of bias (RoB) was assessed for each study using the Cochrane Risk of Bias 2 (RoB2) tool.^36,37 This tool covers randomization, period, and carryover effects (for crossover designs), intervention effects, missing data, measurement of the outcome, and selective outcome reporting. Only 1 study had a crossover design,³⁸ and carryover effects were scored with low risk for that study.

Effect Size Computation

Raw mean change differences (RMCDs) were used to compute the effect size between red meat and control groups in R (version 4.0.2; R Core Team) using the escalc function within the metafor package.³⁹ The analysis began with calculating raw mean changes from pre-test to post-test for both intervention and control groups. This calculation incorporated the pretest SD and a bias correction factor as outlined by Becker.⁴⁰ In a repeated measures design, the same participants are measured repeatedly and, therefore, the effects across several time points within a given study are not independent of each other. To account for this and avoid overestimating the precision of the effects, a pre- and post-test correlation value was included in the analysis. Because of the absence of reported pre- and post-test correlations within the studies, each effect was assigned an estimated correlation of either .90, .94, or .98, depending on the time point of that effect (<8 weeks = .98, 8 to 16 weeks = .94, >16 weeks = .90). This assumption was based on an unpublished data set we generated by using an intervention of increased red meat intake (ISRCTN clinical trial registration no. ISRCTN43345245). To assess the influence of this assumption, alternative correlations of .85 and .90 were also tested across all effects by a sensitivity analysis. Next, the raw mean difference effect size was obtained by subtracting the raw mean change of the intervention group from the corresponding statistic of the control group.⁴¹ The effect size sampling variances were determined by summing the sampling variances of both conditions. A positive effect size indicated an increase in the outcome measures.

Statistical Analysis

All statistical procedures in the study were conducted in R (version 4.0.2; R Core Team). A maximum likelihood multivariate random effects model, implemented within the metafor package,^39,42 was used due to the presence of multiple dependencies in the data (n = 2 outcomes, multiple time points, and several intervention conditions with the same control condition originating from the same study).

To account for the nonindependence of the effect sizes, a variance-covariance matrix was constructed and incorporated into the meta-analytic model.⁴² The necessary correlations between the meta-analyzed outcomes for the variance-covariance matrix were again derived from an unpublished data set we compiled on this topic (r = .18). The same data set was used to estimate the autocorrelation (ie, the correlation of a variable with itself over successive time intervals) between time points measured at 1 week intervals (r = .94), with alternative autocorrelations of .90 and .97 also examined in a sensitivity analysis. The clubSandwich package was used to incorporate a robust variance estimator to improve the accuracy of the estimates, given the uncertainty surrounding the exact magnitude of dependence between the effects.⁴²

Within the multivariate model, random effects were included for each effect size within each study, allowing the effect sizes to correlate and have varying variances. The between-study heterogeneity of the effects was assessed using the parameters τ² and I².^43,44 Notably, the standard heterogeneity statistic (Q) is not applicable to multivariate models; therefore, a likelihood ratio test examining the influence of τ² on both outcomes was conducted to assess between-study heterogeneity. The presence of between-study heterogeneity was considered significant if the likelihood ratio test (χ²) P value was <.05, and the sampling error accounted for less than 75% of the observed variance.^45,46

Moderators were examined using linear regression analysis as univariate variables to elucidate potential reasons for heterogeneous effects among the outcomes. To detect publication bias, a modified version of Egger's test was implemented within the multivariate model, using the SE of the observed outcomes as a predictor, alongside visual inspection of normal and contour-enhanced funnel plots.^47,48 Outlier and influential studies and effects were analyzed using Cook's distances and the distribution of studentized residuals.³⁹

Sensitivity analyses were conducted at multiple levels. Initially, alternative pre-post correlations for calculating effect sizes and varying autocorrelations for computing the variance-covariance matrix were explored. Subsequently, the impact of excluding outlier and influential studies and effects was evaluated (details of the excluded studies and effects are given in Figure S2). Finally, the effect of excluding poor quality studies (ie, studies with a high ROB according to ROB2) was examined. The results of the sensitivity analyses are presented in Table 2.

Table 2.

Sensitivity analysis.

Sensitivity analysis procedure		Serum ferritin (µg L–1)		Hemoglobin (g L–1)
		RMCD	95% CI	RMCD	95% CI
1	Main	1.87	–0.731 to 4.48	2.36	0.706 to 4.02
2	Auto r = 0.85	2.39	0.609 to 3.87	2.39	0.050 to 4.73
3	Auto r = 0.90	2.21	–0.236 to 4.65	2.29	0.653 to 3.93
4	Auto r = 0.94	2.70	0.369 to 5.03	2.24	0.468 to 4.01
5	Pre-post r = 0.85	2.56	0.037 to 5.08	2.20	0.345 to 4.05
6	Pre-post r = 0.90	2.65	0.280 to 5.02	2.22	0.415 to 4.02
7	Outliers/influentials removed	2.72	0.607 to 4.83	2.33	1.00 to 3.66
8	Poor quality studies removed	2.00	–0.288 to 4.29	2.37	0.446 to 4.29

Abbreviation: RMCD, raw mean change difference.

Effect size (reported using Cohen’s d⁴⁹) was presented for biomarkers of iron status that were not meta-analyzed to compare the magnitude of effect between pre- and post-intervention measurements for both conditions. Effect size was calculated using the following formula, as previously described⁵⁰:

$$\mathrm{Cohen}\mathrm{’}\mathrm{s}\mathrm{ }d\mathrm{ }=\frac{\mathit{Mdiff}}{\frac{SD1+SD2}{2}},$$

where, Mdiff represents the absolute difference between the pre- and postintervention means, and SD1 and SD2 denote the SDs of the pre- and post-intervention measurements, respectively.

RESULTS

In total, 10 articles met the inclusion criteria for this review. Concentrations of serum ferritin and Hb were the most reported biomarkers of iron status, appearing in all 10 articles. In the studies reported on in these articles, serum iron concentration was the next most frequently measured biomarker (n = 6 articles), followed by Tf saturation (n = 5), Tf (n = 4), hematocrit (n = 3), and TIBC (n = 3). Because of the limited number of effects (k) for some biomarkers, the final meta-analysis was conducted only for ferritin (k = 25) and Hb (k = 17), including a total of 42 effects from 10 studies.

The total number of participants was 397, 323 of whom were female (81%). Participants had a mean (SD) age of 28.3 (7.8) years. The red meat interventions lasted between 8 and 52 weeks, with a median (IQR) duration of 12 (12 to 19) weeks, and several studies reported data at intermediate time points (Table 3), which were incorporated into the meta-analysis. The quantity of red meat administered to participants each week ranged between 255 g and 1841 g (cooked weight), with a mean (SD) of 732 (497) g. The control conditions comprised habitual dietary intake (n = 3 articles), consumption of a diet low in bioavailable iron (n = 4 articles), provision of a multivitamin supplement (n = 1 articles), and consumption of a diet rich in other types of meats (ie, oily fish/poultry) (n = 2 articles). Adherence to the red meat intervention was clearly reported in only 1 of the included articles.⁵¹ The complete descriptive information of the included articles reporting on applicable studies is presented in Table 3. ^{30,38,51–58}

Table 3.

Characteristics of included studies

Study	Group: no. of participants	Population (age, y)	Baseline ferritin concentration ± SD (µg L–1)	Intervention	Comparator	Outcomes
Blanton, 201352	Int: 22 Con: 21	ID F (21.1)	Int: 33.6 ± 4.8 Con: 28.3 ± 4.2	3 beef (85 g) lunches per wk Durationa: 16 wk Intermediate time points: 8 wk	3 nonbeef (85 g) lunches per wk	Serum ferritin, Hb, hematocrit, serum iron, Tf, Tf saturation
Bodwell et al, 198753	Int: 13 Con: 27	Healthy M (38.6) and ID F (32.1)	Int: 52.5 (26.7-103.1) (M) 25.1 (12.7-49.5) (F)b Con: 52.5 (26.7-103.1) (M) 15.1 (7.7-29.8) (F)b	11 beef patties (4 oz) per wk Durationa: 180 d Intermediate time points: 45, 90, 135 d	Habitual diet	Serum ferritin, Hb
Griffin et al., 201330	Int: 36 Con: 35	Overweight F (22.1)	Int: 45.0 (34.0-56.0) Con: 45.0 (32.0-58.0)c	Red meat (200 g) 4 dinners per wkd Durationa: 52 wk Intermediate time points: 26 wk	1 red meat (80 g) dinner per wk	Serum ferritin, Hb, serum iron, Tf, Tf saturation
Hunt and Roughead, 200054	Int: 14 Con: 17	Healthy M (44)	Int: 118.0 ± 19.0 Con: 100.0 ± 16.0	Beef/pork (260 g) 7 times wk–1, plus a diet high in other sources of bioavailable irond Durationa: 12 wk Intermediate time points: 2, 10 wk	No red meat, plus a diet low in other sources of bioavailable iron	Serum ferritin, Hb, Tf saturation
Hunt, 200355	Int: 18 Con: 18	ID F (32)	Int: 21.0 ± 5.0 Con: 21.0 ± 5.0	Beef/pork (194 g) 7 times wk–1, plus a diet high in other sources of bioavailable irond Durationa: 12 wk Intermediate time points: 2, 10 wk	No red meat, plus a diet low in other sources of bioavailable iron	Serum ferritin, Hb, TIBC, Tf saturation
Johnson et al, 201256	Int: 18 Con: 16	Healthy M and F athletes (21)	Int: 56.7 ± 19.5 Con: 56.1 ± 20.8	9 lean beef sticks (1 oz) per wk, plus a daily multivitamin supplement containing 18 mg of iron as ferrous sulphated Durationa: 8 wk Intermediate time points: None	Daily multivitamin supplement containing 18 mg of iron as ferrous sulphate	Serum ferritin, Hb, hematocrit, serum iron, TIBC
McArthur et al, 201251	Int: 21 Con: 22	ID F (24.6)	Int: 27.3 ± 17.2 Con: 29.5 ± 22.4	Pork (167 g) 3 times wk–1 Durationa: 12 wk Intermediate time points: 4, 8 wk	Habitual diet	Serum ferritin, Hb, serum iron, Tf, Tf saturation
Navas-Carretero et al, 200938	Int/con: 25	ID F (24)	Int: 17.0 ± 11.3 Con: 18.0 ± 11.2	Advice to consume red meat (80 g) 5 times wk–1d,e Durationa: 8 wk with 7 d washout period Intermediate time points: None	Advice to consume 5 portions of oily fish per wk	Serum ferritin, Hb, hematocrit, serum iron, Tf
Patterson et al, 200157	Int: 22 Con: 22	ID F and healthy age-matched control participants	Int: 8.9 ± 3.1 Con: 49.4 ± 28.8	Advice to consume beef/lamb (90 g) 7 times wk–1d Durationa: 12 wk Intermediate time points: None	Habitual diet	Serum ferritin, Hb, serum iron, TIBC
Tetens et al, 200758	Int: 29 Con: 28	ID F (29)	Int: 18.1 ± 9.3 Con: 17.3 ± 9.5	Beef/pork (106.5 g) 7 times wk–1d Durationa: 20 wk Intermediate time points: None	Vegetarian diet	Serum ferritin, Hb

^aTime from pre- to post-intervention measurements.

^bData presented as mean (geometric mean ± 1 SD).

^cData presented as mean (95% CI).

^dRed meat was the primary, but not the only, food source consumed as part of the intervention.

^eCrossover design.

Abbreviations: Con, control group; F, female; Hb, hemoglobin; ID, iron deficient; int, intervention group; M, male; Tf, transferrin; TIBC, total iron binding capacity.

The inter-rater agreement statistics support strong agreement between article reviewers. The absolute agreement between reviewers for all extracted continuous data, using the 2-way mixed effect model and “single rater” unit for the intraclass correlation coefficient, was 0.99 (95% CI, 0.99 to 0.99; P < .001) with a percentage agreement of 92.7%. The inter-rater reliability for moderator coding was in perfect agreement (unweighted Cohen’s κ = 1.00 [95% CI, 1.00 to 1.00]; z = 0; P < .001; 100% agreement).

Finally, the inter-rater agreement applying the RoB tool as an unweighted Cohen’s κ was 0.0364 (95% CI, −0.20 to 0.27; z = 0.307; P = .759), with 64% agreement. All discrepancies were discussed until full agreement was achieved. As shown in Figures 2 and 3, 1 study had a high RoB. This risk was due to the allocation of participants to groups reported as not being randomized. Furthermore, all studies were ranked as having some concerns of bias, because of the effect of the assignment to the intervention. However, this effect is to be expected, owing to the nature of the study designs and the difficulty involved in blinding individuals to food-based nutrition interventions.

Figure 2.

Risk of bias (RoB) of each included study. The RoB of each criterion measured by the Cochrane Risk of Bias 2 (RoB2) tool and the total RoB for each individual study. Graphic created with the RoB2 tool.

Figure 3.

Summary of RoB. Summary of RoB based on each criterion measured by the Cochrane Risk of Bias 2 (RoB2) tool. Graphic created with the RoB2 tool.

Effect of Increased Red Meat Intake on Serum Ferritin and Hb Concentrations

For serum ferritin concentration, 80% of the outcome estimates were positive (favoring the red meat condition), ranging from –4.8 to 17.0. The multivariate model indicated the raw mean change difference from pre- to mid- to post-intervention time points between the treatment and control conditions was 1.87 μg L^–1, but this difference did not differ significantly from 0 (95% CI, –0.73 to 4.48; t = 1.619; P = .139). In addition, heterogeneity was observed (χ² = 38.8; P < .001; τ² = 10.1; I² = 91.4%).

For Hb concentration, 71% of the outcome estimates were positive (favoring the red meat condition), ranging from –2.0 to 5.0. The multivariate model indicated the raw mean change difference from pre- to mid- to post-intervention time points between the treatment and control conditions differed significantly from 0, with a value of 2.36 g L^–1 (95% CI, 0.71-4.02; t = 3.297; P = .011). This meta-analysis also contained heterogeneity (χ² = 288.4; P < .001; τ² = 3.46; I² = 96.3%).

The effect sizes aggregated at the study level (n = 1 effect per study displayed per outcome) and their CIs, as well as the raw mean change difference according to a meta-analytic multivariate model and a 2-level random effects model, are displayed in Figure 4.^{30,38,51–58} Influential studies, outlier analysis, and publication bias are displayed in Figures S2 and S3. The aggregated data set and R code used for analysis can be found on the Open Science Framework website (see the “Data Availability” statement). Additional information can be shared upon request.

Figure 4.

Forest plots for serum ferritin (upper panel) and hemoglobin (lower panel) concentrations. Aggregated study effects are displayed with a raw mean estimate coming from a meta-analytic model and a 2-level random effects model for comparison.

Moderator Analysis

Duration of the intervention was a significant moderator of the impact of increased red meat intake on serum ferritin concentrations (Table 4). The raw mean change difference in serum ferritin concentrations between the treatment and control conditions at various time points significantly improved as the duration between these time points increased (P < .001). Significant moderators of increased red meat intake on Hb concentrations included sex, intervention duration, and the type of control condition (Table 5). Increased red meat intake was more effective at improving Hb concentrations in women compared with men (P = .001). Studies that used habitual diets as the control condition reported larger effects on Hb concentrations than those that used another type of intervention (P < .001). In contrast to ferritin concentrations, the effect of increased red meat intake on Hb concentrations was inversely associated with the duration of the intervention, with shorter duration interventions being associated with marginally better improvements (P < .001).

Table 4.

Univariate results for significant serum ferritin categorical moderator variable

Effect moderator duration, wk	No. of effects	RMCD (μg L–1)	95% CI
<8	5	–1.11	–2.38 to 0.155a
8 to 16	13	2.27	0.87 to 3.67b
>16	7	5.62	0.67 to 10.6c

^{a, b, c}Moderator levels with a noncommon superscript differ significantly.

Abbreviation: RMCD, raw mean change difference.

Table 5.

Univariate results for significant hemoglobin categorical moderator variables.

Effect moderator	No. of effects	RMCD (g L–1)	95% CI
Sex
Male	3	–1.05	–33.0 to 30.9a
Female	13	2.58	1.27 to 3.89b
Baseline iron status
Normal	6	2.01	–0.22 to 4.24
Iron deficient	11	2.56	0.91 to 4.20
Duration, wk
<8	3	2.77	0.63 to 4.92a
8 to 16	11	2.33	–0.32 to 4.98b
>16	3	2.21	0.42 to 3.99a,b
Control condition
Habitual diet	4	4.46	1.60 to 7.32a
Other intervention	13	1.85	–0.31 to 4.01b

^{a, b}Moderator levels with a noncommon superscript differ significantly.

Abbreviation: RMCD, raw mean change difference.

Effect of Increased Red Meat Intake on Other Biomarkers of Iron Status

A summary of the effects of increased red meat intake on the other biomarkers of iron status are listed in Table 6.^{30,38,51,52,54–57} In most studies, increasing red meat intake had a negative effect on serum iron levels (n = 5 of 6 studies), Tf saturation (n = 3 of 5 studies), Tf (n = 3 of 4 studies), and TIBC (n = 2 of 3 studies) but had a positive effect on hematocrit (n = 2 of 3 studies).

Table 6.

Effect sizes (Cohen’s d) of changes in biomarkers of iron status from before to after intervention for each condition.

Reference	Serum iron		Transferrin saturation		Transferrin		Hematocrit		TIBC
	Int	Con	Int	Con	Int	Con	Int	Con	Int	Con
Blanton, 201352	|0.20|↓	|0.07|↑	|0.19|↓	|0.03|↓	|0.01|↑	|0.04|↓	|0.28|↑	|0.26|↑
Hunt, 200355			|0.13|↓	|0.00|					|1.50|↑	|1.25|↑
Johnson et al, 201256	|0.93|↓	|0.72|↓					|0.69|↑	|0.63|↓	|0.42|↓	|0.00|
Patterson et al, 200157	|0.09|↓	|0.48|↓							|0.06|↓	|0.03|↓
Navas-Carretero et al, 200938	|0.07|↓	|0.03|↓			|0.16|↓	|0.04|↑	|0.09|↓	|0.15|↓
Griffin et al, 201330	|0.26|↑	|0.15|↓	|0.33|↑	|0.19|↓	|0.14|↓	|0.00|
McArthur et al, 201251	|0.29|↓	|0.18|↑	|0.20|↓	|0.29|↑	|0.40|↓	|0.36|↓
Hunt and Roughead, 200054			|0.00|	|0.00|

Abbreviations: Con, control; Int, intervention; TIBC, total iron-binding capacity; ↑, increase; ↓, decrease.

DISCUSSION

This systematic review identified 10 articles on intervention studies that have investigated the effect of increased red meat intake on iron status in adults, each of which measured both serum ferritin and Hb concentrations. Results from the meta-analysis revealed that increased red meat intake led to a small, yet statistically significant, improvement in Hb concentrations. Although the exact mechanism is not established, the synergy of micronutrients found in red meat, including iron, zinc, selenium, vitamins B₆ and B₁₂, and dietary folate, are likely to promote Hb synthesis.^24,59,60 Moderator analysis revealed that Hb concentrations were improved to a greater extent in women than men after the intervention, which is consistent with previous findings.¹⁷ Lower concentrations of hepcidin, a hormone that regulates iron homeostasis, facilitates increased iron absorption in women,⁶¹ which may explain the observed sex differences in response to the intervention, given the role of iron in Hb synthesis. These interventions had no significant effect on ferritin concentrations overall, although moderator analysis indicated a small positive effect when an intervention of increased red meat intake lasted 8 weeks or longer. Findings from the present study substantiate those reported in previous literature on observational studies suggesting that red meat can contribute to the maintenance of iron status.¹¹ However, the extent to which these changes in response to increasing red meat intake can lead to clinically meaningful improvements in iron status remains less clear.

Although a variety of population groups featured in the included studies, the majority focused on women of childbearing age, most likely because they are at an increased risk of developing ID due to higher requirements for iron secondary to menstruation and pregnancy.¹ Habitual red meat intake has also been found to be low in this population; for example, 43% of women in the United Kingdom consume <40 g d^–1 red meat,⁶² which highlights the potential value of dietary intervention with increased intake. In young women, 16 weeks of moderate red meat intake (36.5 g d^–1) in the form of beef lunches led to improvements in iron status, with average ferritin and Hb concentrations increasing by 8 μg L^–1 and 2.8 g L^–1, respectively.⁵² However, the number of participants who experienced a clinically meaningful improvement in serum ferritin concentration was, in fact, similar in both the beef and nonbeef lunch groups. Women with a lower baseline iron status had greater increases in markers of iron status, regardless of which intervention group they were in.⁵²

Although we did not find baseline iron status to be a significant moderator of change in serum ferritin concentration, this analysis was based on aggregated group data, which do not always capture individual responses, especially when heterogeneity exists. Multiple studies have demonstrated that individuals with low iron status respond better to treatment given that the primary determinant for iron absorption is the systemic requirement for iron.^7,20,63,64 Thus, a more pronounced effect of increased red meat intake on iron status may have been observed if the present review had focused exclusively on individuals with low iron stores, or if we had used an individual participant data meta-analysis. Conversely, iron status can be improved by increased red meat intake regardless of baseline status.³⁰ A high-protein diet containing approximately 86 g d^–1 red meat resulted in large improvements in serum ferritin concentrations (average change from pre- to post-intervention of 13 μg L^–1) in overweight women with normal baseline iron status.³⁰ This study also showed that the consumption of red meat as part of a weight loss intervention can be beneficial for iron status, which is a noteworthy finding given the association between energy-restricted diets and suboptimal iron status.⁶⁵

The present analysis found duration of intervention was a significant moderator of both ferritin and Hb concentrations, with the response of ferritin concentration to increased red meat intake improving by a small extent after 8 weeks, and as much as 3-fold as the study duration lengthened beyond 16 weeks. Among the included studies, the largest improvement in serum ferritin concentration was observed as 13 μg L^–1 following a 52 week intervention of 4 × 150 g portions of red meat per week.³⁰ In contrast, several studies with interventions of shorter duration, particularly those lasting <12 weeks, reported no meaningful changes in ferritin concentrations from before to after the intervention.^38,54,55

These findings tentatively suggest that longer intervention periods may be necessary to see a beneficial effect of increased red meat intake on markers of iron status. One caveat is that this suggestion is based on data from a relatively small number of effects, and future studies with designs that specifically address the effect of duration of intervention are warranted. However, cross-sectional studies that explore the effects of habitual intake of greater amounts of red meat intake tend to substantiate this contention by often demonstrating a positive relationship between red meat intake and iron status.^11,29 For example, individuals who consumed red meat daily had, on average, a 36% higher serum ferritin concentration than those who did not.⁶⁶ Similarly, serum ferritin concentrations were higher in omnivorous men compared with vegetarian men (141 ± 93 µg L^–1 vs 72 ± 32 µg L^–1, respectively).⁶⁷ However, the same study noted that this directional change was not observed in women, whose iron stores remained low across the population regardless of meat intake (22 ± 13 µg L^–1 vs 27 ± 16 µg L^–1),⁶⁷ whereas another study observed higher serum ferritin concentrations in women who ate greater quantities of poultry and fish (17.5 µg L^–1) compared with red meat (6.8 µg L^–1).⁶⁸ These conflicting results highlight the need for future experimental studies using longer interventions to determine whether the impact of increased red meat intake on iron status increases with extended intervention periods.

Serum ferritin concentration is a more sensitive indicator of iron status than Hb.^35,69 Although some of the included studies demonstrated that increased red meat intake could maintain or slightly improve (avereage change from pre- to post-intervention of 2.23 µg L^–1) ferritin concentrations,^38,52,57 this change is unlikely to be sufficient for individuals with ID, as defined by a ferritin concentration <35 µg L^–1,¹² whose priority should be to increase iron stores to a clinically-meaningful extent ie, return to normal iron status. Currently, oral iron supplementation is a superior option for improving markers of iron status in individuals with ID, with many studies demonstrating its effectiveness.^10,14,15,70 When the effects of iron supplementation (350 mg d^–1 ferrous sulphate containing 105 mg inorganic iron per day) and increased red meat intake (120 g of beef or lamb per day) were directly compared previously, iron supplementation resulted in greater improvements (average change from pre- to post-intervention of 15.8 µg L^–1 vs 2.1 µg L^–1) in serum ferritin concentrations after 12 weeks of intervention.⁵⁷ However, the same study reported that the diet rich in red meat led to continued improvements in iron status after the intervention had finished. During the 6 month follow-up period, participants in the red meat group improved their serum ferritin concentration by a further 4.2 µg L^–1, whereas those in the supplement group experienced a decline of 0.62 µg L^–1.⁵⁷ Thus, for individuals with ID, iron supplementation in conjunction with dietary modifications could be suggested to promote long-term improvements in iron status.

Among the included articles, there was considerable heterogeneity in the types of control conditions used, which may partly explain the mixed results observed across studies. Four studies compared the effect of a red meat–based intervention with that of a diet low in bioavailable iron.^52,54,55,58 In one such study, young women with low iron stores were randomized to a red meat-based diet or a vegetable-based diet for 20 weeks.⁵⁸ Although both groups had a similar iron intake over the intervention period (11.0 mg d^–1 vs 11.7 mg d^–1), ferritin and Hb concentrations declined in women after the vegetable-based diet (average change of –6.1 µg L^–1 and –3.0 g L^–1, respectively), whereas both biomarkers remained unchanged in individuals after the meat-based diet (average change, of 0.2 µg L^–1 and –1.0 g L^–1, respectively). Interestingly, neither group met the current recommended dietary allowance of 18 mg d^–1 iron,⁷¹ despite the red meat group being provided approximately 100 g d^–1 of meat in the form of beef and pork. These findings are consistent with previous work³⁸ and suggest that the bioavailability of the dietary iron consumed may be more important than the absolute quantity of dietary iron.

Some methodological considerations for the included studies are worthy of discussion. In studies that focused on women of childbearing age, factors such as menstrual cycle phase, menstrual blood losses, hormonal contraceptive use, and number of births were often not considered, despite their known impact on iron status.^1,10 Furthermore, iron absorption is influenced by various enhancing and inhibiting factors present in the diet^20–22; thus, the individual dietary behaviors of the study participants likely affected their iron absorption, and subsequently their iron status, throughout the intervention. Although some studies attempted to take this into account and measure the dietary intake of their participants, most relied on self-reported data collection techniques, which are subject to underreporting and participant bias.⁷² Relatedly, the potential impact, if any, of a red meat intervention on iron status is likely mediated by an individual's habitual red meat intake, yet information on participants’ habitual red meat intake was not reported in the majority of the included studies, and it remains unclear whether a given intervention ultimately resulted in an increase in red meat intake. Adherence to the red meat intervention was also inconsistently reported across studies and, therefore, could not be accounted for in the present analysis. Finally, although studies generally reported the quantity and type of meat administered to participants as part of the intervention, little information regarding the quality and nutrient composition of the meat was provided. The quality and nutrient composition of red meat can be influenced by a number of factors, spanning from animal husbandry practices to meat processing techniques.^73–75 For example, meats from grass-fed vs grain-fed animals contain higher concentrations of beneficial fatty acids, such as conjugated linoleic acid and omega-3 polyunsaturated fatty acids, and increased antioxidant content.⁷⁶ Furthermore, different cuts of meats, such as pork, vary in their vitamin content, depending on which part of the animal they are taken from.¹⁹ At present, there is a lack of research on the factors that may impact the iron content of meat, highlighting an important area for future work.

Several potential limitations of the present review and analyses need to be considered. The number of studies included was small, which indicates the need for additional research in this area. Moreover, the quality of the included studies, in terms of RoB, ranged from some risk to high risk, with no study characterized as having a low RoB. One of the key biomarkers assessed was serum ferritin concentration, which is also an acute-phase protein that responds to inflammation, and under this condition, does not accurately reflect iron stores.⁷ Few of the included studies measured ferritin in conjunction with a known inflammatory marker, such as C-reactive protein, in an attempt to adjust for inflammation. Therefore, the potential impact of inflammation on the meta-analyzed ferritin results is unknown, but in mitigation, most of the participants in the included studies were reported to be generally healthy. Finally, factors such as exercise training status and body mass index likely influence the effect of increased red meat intake on iron status, but these were not consistently reported and, therefore, could not be analyzed in the present review.

CONCLUSION

In this systematic review, we meta-analyzed the effects of interventions involving increased red meat intake on iron status in adults. Increasing red meat intake led to a small but significant improvement in Hb concentrations but had no effect on serum ferritin concentrations unless the period of increased intake was 8 weeks or longer, during which there was a small positive effect. Although increasing red meat intake can result in small improvements in iron status, the extent to which such increases are clinically meaningful remains to be established. Moreover, the effect of increased red meat intake on iron status is likely to be influenced by several other factors, such as baseline iron status, habitual dietary behaviors, and intervention type and duration, which all should be measured and considered in future studies to fully understand the effect of this diet-based intervention on iron status.

Author Contributions

This study was conceived and designed by all the authors. Data were collected by L.M., K.V., V.S.F. and B.E. Data were analyzed by L.M. and M.M. Data interpretation and manuscript preparation were undertaken by L.M., M.M. and B.E. All authors have read and approved the final version of the manuscript.

Supplementary Material

Supplementary Material is available at Nutrition Reviews online.

Funding

None declared.

Conflicts of Interest

B.E. reports that related research in his laboratory is supported by funding from Enterprise Ireland through the Meat Technology Ireland technology center (grant TC-2021–0031). Meat Technology Ireland is an industry-academic partnership with the focus of 1 research stream: the benefits, if any, of red meat for human health, but the organization did not fund the work described in this article. The other authors have nothing to declare.

Data Availability

The aggregated data set and R code used for analysis can be found on the OSF website (https://osf.io/9eqf3/?view_only=4ae89edfe0ac42688b33ae736bb2b2b4). Additional data will be made available upon request to the corresponding author.