The HbA1C Paradox: How Iron Deficiency Masks True Glycemic Control

Introduction

Diabetes mellitus has transitioned from a chronic metabolic disorder into a global health crisis, with India frequently cited as the Diabetes Capital of the world due to its staggering disease burden. Projections indicate that the global prevalence will reach 4.4% by 2030, with India’s caseload expected to surge to nearly 79.4 million individuals [1]. Type 2 diabetes mellitus (T2DM), the most prevalent phenotype, is driven by a complex interplay of insulin resistance and progressive beta-cell dysfunction, often exacerbated by rapid urbanization and genetic susceptibility [2].

Recent clinical focus has shifted toward the role of micronutrient metabolism in T2DM pathophysiology, particularly the association between iron homeostasis and insulin resistance. Metabolic syndrome—a cluster of central obesity, hypertension, and hyperglycemia—is increasingly linked to disturbances in iron sequestration [3]. Elevated serum ferritin, a primary biomarker for body iron stores, often correlates with hyperinsulinemia and elevated fasting glucose, suggesting that iron overload may be a silent driver of metabolic decay.

The clinical management of T2DM relies heavily on glycated hemoglobin (HbA1c) as the "gold standard" for monitoring long-term glycemic control [4]. Formed via the non-enzymatic glycation of the hemoglobin beta chain, HbA1c levels typically reflect average blood glucose over the preceding 120 days. However, its diagnostic reliability is often compromised by physiological variables independent of glycemia, such as nutritional anemias, hemoglobinopathies, and chronic kidney disease [5-6].

The mechanistic link between iron and diabetes is rooted in oxidative stress. Excess free iron catalyzes the formation of reactive oxygen species (ROS) through Fenton chemistry, leading to lipid peroxidation and cellular damage. Pancreatic beta cells are particularly vulnerable to this oxidative insult due to their sparse antioxidant defenses, which can result in impaired insulin secretion and apoptosis. Furthermore, iron deposition in hepatic and skeletal muscle tissues impairs glucose uptake and suppresses insulin clearance, creating a vicious cycle of hyperglycemia [5-6].

Conversely, iron deficiency (ID) remains the world’s most prevalent nutritional disorder and has been shown to paradoxically elevate HbA1c levels despite stable glycemia, likely through altered erythrocyte turnover [22-23]. Despite these insights, data regarding the interplay between iron status (ferritin, TIBC, and transferrin saturation) and glycemic control remain inconsistent, particularly within the Indian population [5].

Therefore, the present study seeks to bridge these diagnostic gaps by evaluating the intricate correlation between iron biomarkers specifically serum iron, ferritin, and TIBC and HbA1c levels, thereby refining the accuracy of glycemic monitoring in both anemic and non-anemic type 2 diabetic cohorts.

Materials and Methods

Study Design and Setting

This hospital-based observational study was conducted at tertiary care hospital. The study was executed over an 18-month tenure, spanning from July 2024 to December 2025, after obtaining clearance from the Institutional Ethical Committee (SGRD/IEC/2024-364).

Participants and Eligibility

The study population consisted of patients diagnosed with Type 2 Diabetes Mellitus (T2DM) presenting to the outpatient departments or emergency services. Following a convenience sampling approach, participants were enrolled after providing written informed consent in their native language. The cohort was categorized into two distinct groups: Group A: T2DM patients with concomitant iron deficiency anemia (IDA); Group B: T2DM patients without iron deficiency anemia.

Inclusion Criteria

Patients aged >35 years with a confirmed diagnosis of T2DM.

Exclusion Criteria

To eliminate confounding variables affecting HbA1c or iron metabolism, we excluded patients with Type 1 Diabetes, chronic kidney disease, alcoholic liver disease, pregnancy, hypothyroidism, or known malignancies. Additionally, those with recent histories of trauma, surgery, blood transfusions, or iron supplementation (within the previous 3 months) were excluded. Patients with non-iron deficiency anemias (e.g., hemolytic, macrocytic, or thalassemia) were also excluded.

Diagnostic Definitions

Diabetes was defined according to American Diabetes Association (ADA) criteria: Fasting Plasma Glucose (FPG) ≥126 mg/dL, 2-hour post-prandial glucose ≥200 mg/dL, or HbA1c 6.5%. Iron deficiency anemia was established based on WHO thresholds (Hemoglobin <13 g/dL in men; <12 g/dL in women) alongside clinical evaluation and a comprehensive iron profile (Serum Iron, Serum Ferritin, and TIBC).

Laboratory Procedures

Venous blood (8 mL) was collected via aseptic venipuncture after an 8-hour fast. Samples were partitioned for specific analyses: Hematological Indices: Analyzed using a Sysmex automated hematology analyzer; Glycemic Status: HbA1c was quantified via the D-10 Hemoglobin Testing System; Iron Dynamics: Serum iron, ferritin, and TIBC were measured using the VITROS 5600 Integrated System.

Statistical Methods

Statistical analysis was performed using MS-excel. Continuous variables are presented as Mean±Standard Deviation (SD), while categorical data are expressed as frequencies and percentages. The student’s t-test was employed to compare mean values between Group A and Group B. The Chi-square test (or Fisher’s exact test) evaluated categorical associations. A p-value <0.05 was predefined as the threshold for statistical significance.

Results

The comparison between iron deficiency anemia (IDA) and non-IDA cohorts in this diabetic population reveals a significant clinical paradox: while both groups maintain statistically similar fasting blood glucose levels (p=0.257), the IDA group exhibits significantly higher HbA1c values (9.52±2.17% vs.8.76±2.25%, p=0.018), suggesting that iron depletion serves as a confounding factor that artificially inflates glycated hemoglobin readings (Table 1). Demographic analysis shows a distinct female predominance in the IDA group (p=0.017), alongside a markedly higher prevalence of diabetic neuropathy (78.6% vs. 63.2%; p=0.019), indicating that iron deficiency may exacerbate microvascular complications or serve as a marker for advanced disease progression. Ultimately, the data underscores the necessity of clinical caution when interpreting HbA1c in anemic patients, as the spurious elevation of this biomarker may lead to the mismanagement of glycemic control in those with comorbid nutritional deficiencies.

The hematological and iron profile comparison reveals a highly significant divergence between the two groups across all measured parameters (p< 0.001), confirming a profound state of iron deficiency anemia (IDA) in the study cohort (Table 2). The IDA group is characterized by a severe reduction in hemoglobin (8.15±1.21 g/dL) and microcytic hypochromic indices, as evidenced by markedly lower Mean Corpuscular Volume (MCV) and Mean Corpuscular Hemoglobin (MCH) compared to the non-anemic controls. This cellular depletion is mirrored by a collapse in biochemical iron stores, with serum iron and ferritin levels in the IDA group dropping to nearly half of those seen in the non-IDA group, while Total Iron-Binding Capacity (TIBC) rises sharply to 446.44±73.62 µg/dL as a compensatory physiological response. Collectively, these data establish a clear biochemical signature of advanced iron exhaustion, providing a robust statistical baseline for evaluating the secondary metabolic impacts observed in the study population.

The correlation matrix in table 3 reveals that while fasting blood sugar maintains the strongest positive association with HbA1c (r=+0.526, p<0.001), iron-related parameters exert a significant and independent influence on this glycemic marker. HbA1c demonstrates a notable positive correlation with Total Iron-Binding Capacity (TIBC) and significant inverse relationships with serum iron, hemoglobin, and MCH, suggesting that as iron stores deplete, HbA1c levels rise disproportionately. Interestingly, serum ferritin failed to reach statistical significance (p=0.089), likely reflecting its variability as an acute-phase reactant in diabetes, whereas the consistent negative correlations with red cell indices (p<0.05) emphasize that iron deficiency serves as a critical non-glycemic modulator of HbA1c.

Discussion

The current cross-sectional observational study investigated the relationship between iron deficiency markers serum iron, serum ferritin, and total iron-binding capacity (TIBC)and HbA1c levels among 190 patients with Type 2 Diabetes Mellitus (T2DM). The results demonstrate a statistically significant association between iron deficiency anemia (IDA) and elevated HbA1c (p = 0.018), which appears to occur independently of actual glycemic control, as fasting blood sugar levels were comparable between groups (p = 0.257; Table 1).

The study population had a mean age of 61.92±10.88 years, with the majority of patients falling within the 50–59 age bracket, consistent with the epidemiological peak of T2DM driven by age-related beta-cell decline. A marked female predominance was observed in the IDA group (72.8% vs. 56.3% in Non-IDA; p = 0.017), a sex-specific distribution that mirrors findings by Kumar et al.,[7] and Chowdhury [8], likely reflecting the higher vulnerability of women to iron depletion due to nutritional habits and reproductive history.

The mean duration of diabetes in this cohort was approximately 7.96±6.32 years. While the IDA group trended toward a longer duration of illness (8.75±6.51 years) compared to the non-anemic group (7.02±6.00 years), this did not reach statistical significance (p=0.061). While Raj and Rajan, [9] noted that ferritin levels might evolve with disease chronicity, our data suggests that iron deficiency in T2DM is an independent clinical entity, likely driven by multifactorial triggers such as chronic inflammation or subclinical nephropathy rather than disease duration alone.

A primary finding of this research is the dissociation between HbA1c and glycemic markers. The IDA group exhibited a significantly higher mean HbA1c (9.52±2.17%) compared to the Non-IDA group (8.76±2.25%; p = 0.018), despite comparable Fasting Blood Sugar levels (p = 0.257). This discrepancy indicates that in iron-deficient states, HbA1c may be falsely elevated.

The physiological mechanism involves altered red blood cell (RBC) kinetics; when iron is deficient, the average lifespan of an RBC is prolonged, extending the window for non-enzymatic glycation of hemoglobin. This conclusion is corroborated by Çetinkaya Altuntaş et al.,[4] that reported significant HbA1c elevation in IDA patients despite normal blood glucose. Similarly, Taggarshe Surkunda et al.,[10] concluded that HbA1c loses diagnostic reliability in the presence of coexisting IDA, as it may misclassify patients into the diabetic range.

The hematological profile confirmed classic microcytic hypochromic features in the IDA group. Hemoglobin (8.15±1.21 g/dL), MCV (73.76±5.30 fL), and MCH (24.31±3.10 pg) were all significantly lower compared to the Non-IDA group (p<0.001; Table 2). Correlation analysis (Table 3) showed that HbA1c has a significant negative correlation with Hemoglobin (r = -0.161, p = 0.027) and MCH (r=-0.174, p=0.017), a trend supported by Jyothsna et al.,[11] reported an inverse relationship (r=-0.629) in similar cohorts.

Regarding iron kinetics, serum iron was drastically reduced in the IDA group (30.89±6.08 µg/dL vs. 58.00±20.63 µg/dL; p<0.001), while TIBC was significantly elevated (446.44±73.62 µg/dL vs. 288.97 ±65.92 µg/dL; p<0.001). While serum ferritin was significantly lower in the IDA group (93.33±92.53 ng/mL; p < 0.001), it showed high variability. This reflects ferritin's dual nature as an iron store and an acute-phase reactant. As noted by Raj and Rajan,[9] chronic low-grade inflammation in T2DM can paradoxically elevate ferritin even when functional iron is low.

The prevalence of IDA in this study was high (54.2%), which is consistent with Bansal et al.,[3] who reported an incidence of 54% in hospitalized patients. Notably, there was a significant association between IDA and diabetic neuropathy, which affected 78.6% of the IDA group compared to 63.2% of the Non-IDA group (p=0.019). This supports the observation by Ahmed et al.,[2] that iron deficiency may exacerbate microvascular complications through neuronal hypoxia, and that iron supplementation could potentially ameliorate disease progression in such patients. These findings emphasize the necessity of correcting iron deficiency to ensure accurate HbA1c interpretation and to mitigate microvascular risks.

Conclusion

The present study confirms that iron deficiency anemia significantly interferes with HbA1c measurements, causing a false increase in this primary glycemic biomarker despite stable fasting blood sugar levels. The strong inverse correlation between iron stores and HbA1c suggests that altered erythrocyte kinetics in anemic states prolongs the window for hemoglobin glycation. Given the high prevalence of iron deficiency in the diabetic population, particularly among women, correcting iron status is essential for accurate glycemic assessment. Addressing this nutritional deficit may not only refine diagnostic precision but also help in better managing microvascular complications like neuropathy.

Declarations

Ethics Approval and Consent to Participate

All procedures followed the ethical standards of relevant institutional committees and the Declaration of Helsinki. The institutional review board provided formal approval where required. Patients or their legal guardians gave written informed consent prior to procedures.

Consent for Publication

Written informed consent for publishing clinical details and images was obtained from all participants or their legal representatives. All personal identifiers were fully anonymized to protect privacy.

Data Availability

Datasets are available from the corresponding author on reasonable request, subject to institutional and ethical guidelines.

Competing Interest

The authors declare no financial or personal conflicts of interest related to this manuscript.

Funding

No funding was received from public, commercial, or non-profit sources.

Authors’ Contributions

All authors contributed to study design, data collection, analysis, manuscript drafting, and revision. Each reviewed and approved the final version.