Systematic review and meta-analysis: influence of iron deficiency anemia on blood glycosylated hemoglobin in diabetic patients
Original Article

Systematic review and meta-analysis: influence of iron deficiency anemia on blood glycosylated hemoglobin in diabetic patients

Ling Kuang1,2#, Wenxian Li3#, Guochao Xu4, Min You3, Wenqiao Wu5, Chenglong Li2,6

1Operation Department, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China; 2Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China; 3Stem Cell and Regenerative Medicine Research Center, Sichuan Neo-Life Stem Cell Biotech Inc., Chengdu, China; 4Sichuan Umbilical Blood Bank, Sichuan Neo-Life Stem Cell Biotech Inc., Chengdu, China; 5Flow Cytometry Room, Chengdu Neo-Life Hope Medical Laboratory Inc., Chengdu, China; 6Department of Hematology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China

Contributions: (I) Conception and design: L Kuang, W Li, C Li; (II) Administrative support: C Li; (III) Provision of study materials or patients: W Li, G Xu, M You, W Wu; (IV) Collection and assembly of data: All authors; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

#These authors contributed equally to this work.

Correspondence to: Chenglong Li. Department of Hematology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, 32 West Section 2, First Ring Road, Qingyang District, Chengdu 610072, China. Email: lichenglong84@163.com.

Background: Diabetes is a common metabolic disease with an increasing incidence in middle-aged and elderly people in recent years. Chronic hyperglycemia is the basic feature of diabetes, which can cause long-term damage to eyes, kidneys, nerves, heart, and blood vessels, resulting in functional decline or even failure. Glycosylated hemoglobin (HbA1c) can be used as an indicator of an individual’s blood sugar status over the past 3 months; however, it is slightly affected by ischemic anemia.

Methods: The data retrieval was performed in the databases of PubMed, Embase, and Ovid-Medline from their inception to April 2021, including keywords such as iron deficiency anemia (IDA), diabetes, HbA1c, immunoassay, and ion-exchange chromatography. After passing of sensitivity and heterogeneity analysis, Review Manager 5.3 was employed for meta-analysis.

Results: A total of 6 studies were included in this paper. The analysis results showed that IDA could be considered to have an impact on HbA1c outcomes in non-diabetic populations. In people with diabetes, IDA is not thought to have an impact on HbA1c outcomes.

Discussion: A total of 6 articles were included to discuss the effects of IDA on blood HbA1c in diabetic patients. The study found that when patients with diabetes were tested for blood sugar, the HbA1c did not accurately reflect their blood sugar control over the past 3 months.

Keywords: Iron deficiency anemia (IDA); glycosylated hemoglobin (HbA1c); immune method; iron exchange separation; meta-analysis

Submitted Sep 09, 2021. Accepted for publication Nov 09, 2021.

doi: 10.21037/apm-21-2944

Introduction

Diabetes mellitus (DM) is a syndrome of metabolic disorder caused by various influencing pathogenic factors, resulting in islet dysfunction, insulin resistance (IR), and so on, resulting in sugar, protein, fat, water, and electrolyte imbalance (1). The etiology may be related to genetic factors, immune dysfunction, microbial infection and its toxins, among others (2,3). Chronic hyperglycemia is a basic feature of diabetes which can cause long-term damage to the eyes, kidneys, nerves, heart, and blood vessels, resulting in their compromised function and even failure (4,5). According to the World Health Organization (WHO), the number of people with diabetes has nearly quadrupled in the last 40 years (6). Complications of diabetes can lead to heart attack, stroke, blindness, kidney failure, and amputation (7). Diabetes directly caused 1.5 million deaths in 2012. Therefore, accurate detection and strict control of blood sugar have become a very important and urgent matter (8).

Glycosylated hemoglobin (HbA1c) is a hemoglobin (Hb) composed of glycosylation of valine residues at the NH2 end of globin β chain, which can be used as an indicator of blood glucose status in patients over the past 3 months (9,10). Currently, HbA1c is widely accepted as a mean glycemic index, a measure of the risk of diabetes complications, and a measure of the quality of diabetes care (11). The measurement of HbA1c is attractive for diagnostic use because the single number provides a comprehensive assessment of glycemic control levels; however, it has some inherent limitations. Several factors unrelated to blood sugar can falsely decrease or increase HbA1c test results (12,13). In addition, any factor that reduces the mean life expectancy of red blood cells, regardless of the assay used, mistakenly reduces the test results for HbA1c (14).

Iron deficiency anemia (IDA) is caused by the insufficient intake or excessive loss of iron in the body. Iron storage in the body is reduced when Hb appears, which can be of great harm to health (15). Abnormal iron metabolism is related to organ diseases such as liver and heart (16). The latest research suggests that abnormal iron metabolism plays an important role in the pathogenesis of IR and diabetes (17). An anemic state in which serum iron and blood cell indicators are normal is called a potential iron deficiency state (18,19). Excessive iron load can damage pancreatic β cells, trigger peripheral IR, and promote the occurrence of diabetes and its complications (20). The phlebotomy therapy to reduce iron load and the use of iron chelating agents can reduce IR, reduce glycated Hb, improve abnormal blood lipid metabolism, and delay the occurrence and development of diabetes complications (21). At present, there is no uniform conclusion on the changes of serum HbA1c levels in IDA patients. Studies have shown that serum HbA1c levels in patients with diabetes and IDA are remarkably lower than healthy people (22). In non-diabetic people, IDA can increase serum HbA1c levels (23,24). Of course, studies have confirmed that IDA has no considerable correlation with serum HbA1c levels (25).

In order to explore the correlation between IDA and HbA1c, provide more powerful information for defining the relationship between the two. By strictly evaluating and analyzing the existing case-control studies on the effect of IDA on HbA1c, this paper aimed to evaluate whether IDA can elevate HbA1c, so as to provide a basis for reasonable clinical evaluation of HbA1c in IDA patients.

We present the following article in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting checklist (available at https://dx.doi.org/10.21037/apm-21-2944).

Methods

Literature search strategy

A comprehensive and systematic literature search was conducted based on the Cochrane Handbook of Systematic Reviews of Interventions (https://training.cochrane.org/handbook), with reporting meta-analyses following PRISMA. We searched the database of PubMed, PubMed, Embase, and Ovid-Medline, scientific conferences, and established articles. All the studies included patients with diabetes. Keywords and medical headings used in specific searches included the following: “iron deficiency anemia (IDA)”, “iron deficiency”, “iron therapy”, “iron supplementation”, or “HbA1c”. The articles included were related to IDA and diabetes.

Inclusion and exclusion criteria for articles

Articles were included in the meta-analysis if they met the following inclusion criteria: (I) the study population was non-pregnant adults over 18 years of age; (II) the case group consisted of patients with a definite diagnosis of IDA who had not received iron therapy; (III) controls were healthy individuals without IDA or patients with other diseases that did not affect the study; (IV) study type: case-control study or cohort study; (V) the literature was complete or the data required for analysis could be extrapolated from the data given; (VI) English language, regardless of location.

Articles were excluded from the meta-analysis if they met any of the following exclusion criteria: (I) the study population included children under 18 years of age and pregnant women; (II) studies without control groups or cross-sectional studies; (III) secondary studies such as reviews; (IV) studies that were repeatedly published, of poor quality, inconsistent in study type, with too little information, and for which there was a lack of available data; (V) data prior to 1990 (data prior to 1990 were not included due to considerable differences in the methods used to measure HbA1c before and after 1990).

Literature screening

The titles and abstracts were firstly screen by two researchers independently according to the inclusion criteria, followed by data extraction and quality evaluation. If there was inconsistency of evaluation between the researchers, they consulted with other researchers to further resolve the discrepancy according to the literature and original data. When the title and abstract met the literature requirements, the full text was retrieved for data extraction. Note express 2.0 was used for literature management and deletion of duplicate literature. The inclusion of literature was conducted according to the inclusion and exclusion criteria mentioned above and relevant literature was searched.

Data extraction

The two researchers independently extracted relevant information from all eligible studies using a predefined data extraction table: author, year of publication, sample size, age, country, gender, degree of disease, and course of disease. In the case of missing data, the researchers attempted to contact the original authors of the literature via email. If the data were not available, the Cochrane evaluation manual was used for relevant interpretation, such as the calculation of standard deviation of continuous data.

Quality assessment

In order to improve the quality of the reviewed literature, the quality was assessed in accordance with the “risk of bias assessment” recommended in version 5.3 of the Cochrane Systematic Review Handbook. The evaluation includes the following seven items: (I) which random method was used; (II) whether allocation concealment was used; (III) implementation of the blind method between participants and researchers; (IV) evaluation of the effect of the blind method; (V) risk of bias assessment of the randomized controlled trial (RCT), “meet” indicates that bias was small “not satisfied” referred to high risk of bias, the study did not include a fully detailed report, if there was no mention of random sequence generation, allocation concealment, blinding, the risk was classified as unknown. A score of 1–3 in the 4 dimensions of tracking/exit was considered low quality, and a score of 4–7 was considered high quality.

Data analysis

A forest plot was drawn to present each individual study’s results, combining those articles with corresponding confidence intervals (CIs). After no overlap was suggested among CIs of the included articles, there was certain statistical inhomogeneity among these articles. In further subgroup analysis, it was necessary that the stochastic and fixed models were combined with acceptable inhomogeneity. Subgroups were assigned regarding different designs, then the impact size of which was able to be overlooked. When the inhomogeneity among studies could not be ignored when different properties were investigated, some different properties were excluded from analysis in order to address the inhomogeneity.

Sensitivity analysis was conducted, which aimed to address if individual studies affected the overall results of the portfolio. Each study was removed one at a time. The results of each study were compared with the individual results to confirm whether the results were the same based on the results of the remaining studies. Generally speaking, it was found that an individual study impacted the comprehensive study in the following two circumstances. The presumption of the size of the combined effect was 95% of the size of the combined effect if a study was deleted. The results yielded remarkably different results after the deletion of one study. The sensitivity of the combined results was not stable if one study affected the overall results with only limited difference. In the opposite circumstances, the sensitivity was stable and the conclusion was correct.

Statistical analysis

Review Manager 5.3 (provided by the Cochrane Collaboration) was used for data processing in this systematic review, and the test level was 0.05. When heterogeneity (I2) <50% and P>0.05, no statistical heterogeneity was determined among trials, and the fixed effects model was selected for meta-analysis. When I2≥50% and P≤0.05, statistical heterogeneity was considered among trials, and the random effects model was selected for meta-analysis. The combined effect size of the two groups of evaluation index data was odds ratio (OR) value and its 95% CI, and the forest map was drawn according to the integrated system evaluation results to display the research conclusions. The results with high heterogeneity were analyzed by eliminating articles one by one to explore the possible sources of heterogeneity, and the sensitivity analysis was performed on the results.

Results

Literature search results

A total of 1,587 relevant articles were retrieved in this study, of which 984 were retrieved from PubMed, 545 from Embase, and 303 from Ovid-Medline database. After reading of titles and abstracts, 306 articles that clearly did not meet the inclusion criteria were excluded. After reading of full texts, 50 articles were excluded, and 6 articles (26-31) that met the inclusion criteria were finally included (Figure 1, Table 1).

Figure 1 Literature retrieval process. *, consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers); **, if automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools.

Table 1

Basic characteristics of the included literature

First author Published year Experimental group Control group Study population Age (years) Hb (g/dL) Detection method
Coban E (26) 2004 50 50 Non-DM 35.7±11.9 10.8±1.2 IT
Shanthi B (27) 2013 50 50 Non-DM 43.52±7.79 10.6±1.4 IT
Silva JF (28) 2016 61 61 Non-DM 48±14/49±14 13.2±1.1/9.4±1.9 HPLC, IT
Tarim O (29) 1999 11 26 T1DM 10.7±4/10.1±4.4 T1DM: 13.5±0.9 HPLC
Non-DM Non-DM: 10.6±1.8
Christy AL (30) 2014 70 50 DM 55.46±11.66 12.54±1.4 HPLC
Urrechaga E (31) 2018 410 357 DM >18 Unable to extract HPLC

Hb, hemoglobin; DM, diabetes mellitus; T1DM, type I diabetes mellitus; IT, immunization; HPLC, high performance liquid chromatography.

Bias-risk assessment of included articles

The Cochrane Handbook (version 5.0.2) of the systematic review writing manual was used to evaluate the risk of bias in the 8 articles included in this study. Review Manager 5.3 was employed to output the risk of bias chart (Figures 2,3).

Figure 2 The bias-risk assessment diagram of the included articles.
Figure 3 The bias evaluation bar graph of the included articles.

The Newcastle-Ottawa scale (NOS) was used to evaluate the quality of each included article, and the results are shown in Table 2. It can be seen that the 8 articles included in the study all had a low risk of bias, which met the requirements of subsequent analysis.

Table 2

Quality evaluation of the included literature

First author Representativeness of cases Control selection Definition of contrast Comparability Methodologies for leak investigation and assessment Whether the investigation methods of the two groups are appropriate Total score
Coban E (26) Yes No Yes Yes Yes Yes 6
Shanthi B (27) Yes No Yes Yes Yes Yes 5
Silva JF (28) Yes Yes Yes Yes Yes Yes 7
Tarim O (29) Yes No Yes Yes Yes Yes 7
Christy AL (30) Yes No No Yes No Yes 8
Urrechaga E (31) Yes Yes Yes Yes Yes Yes 6

Meta-analysis of the influence of IDA on Hb in diabetic patients

A total of 4 articles met the requirements. For the index of serum Hb (P<0.00001; I2=93%) in diabetic patients with IDA, data were combined between studies using the random effects model. The results showed that the experimental group was comparable to the control group. The difference in Hb content [mean difference (MD) =−2.00; 95% CI: −2.86 to −1.14; P<0.00001] between the two groups was statistically substantial, as shown in Figure 4.

Figure 4 Forest plot for the serum Hb in diabetic patients with IDA. Hb, hemoglobin; IDA, iron deficiency anemia.

Using the serum Hb of diabetic patients with IDA as the index, the results of the inverted funnel plot showed that the scattered points of the participants were roughly funneled downward. On the horizontal axis, they were arranged roughly symmetrically. Thus, it was suggested that publication bias was not obvious, as shown in Figure 5.

Figure 5 Funnel plot for the serum Hb in diabetic patients with IDA. Hb, hemoglobin; IDA, iron deficiency anemia.

Meta-analysis of the influence of IDA on HbA1c in diabetic patients

A total of 6 articles met the requirements. For the index of serum HbA1c (P<0.00001; I2=98%) in patients with DM complicated with IDA, data were combined between studies using the random effects model, and the results showed that compared with the control group, there was a statistically substantial difference in the content of HbA1c between the two groups (MD =0.84; 95% CI: 0.06 to 1.61; P=0.04), as shown in Figure 6.

Figure 6 Forest plot of serum HbA1c in diabetic patients with IDA. HbA1c, glycosylated hemoglobin; IDA, iron deficiency anemia.

Using the serum HbA1c of diabetic patients with IDA as the index, the results of the inverted funnel plot showed that the scattered points of the participants were roughly funneled downward. On the horizontal axis, they are arranged roughly symmetrically, which suggested that publication bias was not obvious, as shown in Figure 7.

Figure 7 Funnel plot of serum HbA1c in nondiabetic patients with IDA. HbA1c, glycosylated hemoglobin; IDA, iron deficiency anemia.

Discussion

HbA1c is the product of non-enzymatic glycosylation between the free amino group of Hb and glucose (32). The n-terminal valine amino of the β chain of Hb is most exposed to plasma glucose, and its binding with glucose is an important component of glycosylated hemoglobin, known as HbA1c (33). The survival time of red blood cells in the human body is about 120 days, and HbA1c in the blood during erythropoiesis is maintained at a certain level, so the HbA1c can be 3 to 4 months before blood plasma glucose average, and the time of blood collection, blood collection state, and other factors, such as whether fasted or not has nothing to do with its reading, therefore, it can reflect the mean value of long-term plasma glucose in diabetic patients (34,35). The current detection methods of HbA1c can be divided into one item according to the difference in the structure of HbA1c, such as immunoassay, affinity chromatography, ion capture, and so on (36). At present, HbA1c is not only an observation index of mean plasma glucose in diabetic patients, but has also been used in the diagnosis of diabetes in European and American countries. The use of HbA1c has greatly increased, mainly because its determination does not require fasting and the intra-individual variability is relatively low (37). Diseases affecting the life span of Hb are thought to affect the results of blood A1c measurement. Iron deficiency anemia (IDA), the most common anemia disease, has a wide range of patients. Whether the results of blood A1c measurement are accurate for these patients with iron deficiency anemia accurate, Whether the use of HbA1c to assess blood glucose levels in this group of patients is unclear, so further studies are needed to determine whether IDA has an impact on blood HbA1c measurements (38).

In summary, for patients with IDA, the possibility of a false increase in HbA1c must be taken into account when interpreting the clinical results, and should be supplemented by other tests, such as HbA1c. For patients with abnormal HbA1c results, clinicians should comprehensively consider the interfering factors of HbA1c measurement, conduct a comprehensive analysis of the results, and perform relevant necessary examinations to check for anemia and the type of anemia. Caution should be exercised before changing treatment, as anemia may exaggerate a patient’s blood sugar status.

Conclusions

In this paper, in order to explore the IDA in patients with diabetes, we explored the effect of serum concentration of HbA1c in patients with diabetic and non-diabetic patients. To test the serum levels of the effect of HbA1c, detection methods, including immune method and ion-exchange chromatography were analyzed using meta-analysis. A total of 8 articles were included in the meta-analysis, and the results indicated that in the non-diabetic population, IDA can falsely increase the level of HbA1c. The mechanism of the increase of HbA1c in IDA requires further study.

Acknowledgments

Funding: The study was supported by Sichuan Science and Technology Program (2020YFS0434).

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://dx.doi.org/10.21037/apm-21-2944

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/apm-21-2944). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)

Cite this article as: Kuang L, Li W, Xu G, You M, Wu W, Li C. Systematic review and meta-analysis: influence of iron deficiency anemia on blood glycosylated hemoglobin in diabetic patients. Ann Palliat Med 2021;10(11):11705-11713. doi: 10.21037/apm-21-2944

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