Immature reticulocyte fraction (IRF) and its correlation with hematological parameters: A comprehensive analysis across six age groups

Abstract

The immature reticulocyte fraction (IRF) reflects early erythropoietic activity and is used to monitor hematopoietic function clinically. However, the physiological behavior of the IRF in healthy individuals of different ages has not been thoroughly investigated. This study evaluates age-related variations in the IRF and its correlations with hematological parameters across a wide age spectrum in healthy individuals. This cross-sectional study included 853 healthy individuals categorized into 6 age groups: <8 days, 8 to 30 days, 30 days to 1 year, 1 to 16 years, 16 to 55 years, and >55 years. Correlation analyses were performed using data obtained from a high-precision hematology analyzer (Sysmex XN-Series), which applies fluorescence flow cytometry and impedance technology for reticulocyte quantification, to examine the correlation between IRF and hematological parameters (hemoglobin [Hb], HCT [hematocrit], red blood cell count [RBC], white blood cell count, red cell distribution width, reticulocyte hemoglobin content, reticulocyte percentage, and leukocyte subtypes). IRF levels showed significant differences between age groups (P < .001). In neonates group, IRF showed a positive correlation with white blood cell count (r = 0.64, P < .001) and neutrophil count (r = 0.65, P < .001). The negative correlation with Hb and RBC became more pronounced in older age groups (Hb: r = –0.43; RBC: r = –0.32; P < .001). A positive and significant relationship with red cell distribution width was observed in all age groups (r = 0.30–0.54, P < .001). IRF shows different age-related connections with hematological parameters. These connections reflect the changing dynamics of erythropoiesis and bone marrow activity throughout life. Using age-specific reference frameworks to incorporate IRF into clinical assessments may improve the detection and interpretation of subclinical changes in erythropoietic function.

1. Introduction

Hematologic parameters are of paramount importance in diagnosing, predicting the outcome of, and monitoring the response to treatment of systemic diseases. A variety of biochemical and genetic mechanisms govern the production and maturation process of red blood cells, which is known as erythropoiesis.^[1] Reticulocytes, the immature form of erythrocytes, are produced in the bone marrow and transform into mature erythrocytes after being released into the circulatory system. The immature reticulocyte fraction (IRF) is the youngest, most RNA-rich population of reticulocytes in circulation.^[2] Recent studies have demonstrated that the IRF can serve as an early, dynamic indicator of erythropoietic activity with potential applications in monitoring various clinical conditions, particularly hematologic diseases. Bashir, abdurrahman.^[3]

Erythropoiesis, the process of red blood cell production, is a critical regulatory process involving the hormone erythropoietin. This process plays a pivotal role in maintaining oxygen balance within the body.^[4] IRF levels are more widely used to monitor bone marrow suppression and recovery, especially in patients undergoing chemotherapy, to assess inflammatory conditions, and to differentiate between various types of anemia.^[5]

In the contemporary medical context, automated hematology analyzers facilitate expeditious and dependable IRF measurements. Recent analyzers have been developed that can quantitatively assess immature reticulocytes using advanced technologies such as fluorescence flow cytometry.^[6]

In addition to hematologic diseases, the clinical use of IRF is widely employed in many diseases, including chronic kidney disease, inflammatory diseases, sepsis, and hematologic malignancies.^[7]

The clinical significance of IRF is further accentuated by its correlation with other hematologic parameters. Correlations with parameters such as reticulocyte percentage (%RET), hemoglobin (Hb), HCT, white blood cell count (WBC), and red cell distribution width (RDW) provide a more nuanced understanding of the role of IRF in assessing bone marrow function and erythropoiesis.^[8]

The objective of this study was to examine the correlations between IRF and other hematological parameters according to age groups and to emphasize the clinical importance of these correlations. The central objective of this study is to elucidate the alterations in hematopoiesis that occur with advancing age. To this end, we will undertake a comprehensive analysis of the correlation between IRF and a range of parameters, spanning from newborns to older adults. Additionally, the utilization of these findings in clinical practice settings and the role of IRF in the diagnosis and management of hematologic disorders will be deliberated.

2. Materials and methods

2.1. Study design

This cross-sectional study was conducted using laboratory data obtained from the Laboratory Information Management System of the Ankara University Faculty of Medicine. Reticulocyte test results recorded between January 2022 and December 2023 were retrospectively reviewed. The aim was to evaluate age-related variations in the IRF and its correlations with hematological parameters in a healthy population.

2.2. Sample characteristics

A total of 853 healthy individuals were included in the final analysis. The study population was stratified into 6 distinct age groups to analyze age-related variations in hematological parameters. The age groups were defined as follows: <8 days (neonates), 8 to 30 days (infants), 30 days to 1 year (early childhood), 1 to 16 years (childhood and adolescence), 16 to 55 years (adults), and >55 years (elderly). This age-based classification allowed for a detailed examination of how hematological parameters, including IRF, vary across different stages of life.

2.3. Sample selection

All available reticulocyte and complete blood count data from the study period were screened. To minimize confounding factors and ensure inclusion of physiologically healthy individuals, the following exclusion criteria were applied:

• Hematology or oncology inpatients and outpatients.

• Individuals admitted to intensive care units.

• Patients with known hematologic disorders, chronic diseases, systemic illnesses, or active infections.

• Cases with metabolic or inflammatory conditions that could influence erythropoiesis.

• Neonates or infants with any suspected or documented underlying medical condition.

After applying these exclusion criteria, only subjects confirmed as clinically healthy were included. The final dataset consisted exclusively of outpatients presenting for routine, nonurgent laboratory evaluations (e.g., periodic health assessments, pre-visit laboratory workup, or nonspecific clinical complaints).

2.4. Variables selection and definitions

The study focused on evaluating the correlations between IRF and various hematological parameters. These parameters included complete blood count parameters such as WBC, Hb, red blood cell count, HCT, mean corpuscular volume, mean corpuscular hemoglobin concentration, platelet count, %RET, reticulocyte hemoglobin content (CHR), RDW, and mean platelet volume. Additionally, the study analyzed leukocyte subpopulations, including Neut# and Neut%, Lymph# and Lymph%, Mono# and Mono%, Eos# and Eos%, and Baso# and Baso%.

2.5. Analytical method

All hematological parameters, including IRF, were measured using the Sysmex XN series automated hematology analyzer. The Sysmex XN series is a state-of-the-art device known for its high precision and accuracy in measuring a wide range of hematological parameters. It utilizes advanced technologies, such as fluorescence flow cytometry and impedance methods, to provide detailed and reliable results for reticulocyte analysis, including the IRF. The device’s ability to accurately measure IRF and other parameters made it an ideal choice for this study.

2.6. Statistical analysis

Data distribution was assessed using descriptive statistics. Due to non-normal distribution of several hematological parameters, Spearman rank correlation coefficient was used to evaluate associations between IRF and other variables. Correlation analyses were performed separately for each age group to determine age-specific physiological trends.

Categorical variables were summarized as frequencies and percentages; continuous variables were presented as medians and interquartile ranges (IQR). Statistical significance was defined as P < .05. All analyses were performed using standard statistical software.

2.7. Ethic approval

The study was conducted in compliance with ethical guidelines, and patient data were anonymized to ensure confidentiality. Approval for the study was obtained from the relevant institutional review board (IRB) or ethics committee of Ankara University Faculty of Medicine. 2025000322-2, 2025/322. The use of anonymized data ensured that patient privacy was maintained throughout the study.

3. Results

The study analyzed the correlations between IRF and various hematological parameters across 6 distinct age groups (Table 1). The findings are summarized below, highlighting the key correlations and their clinical significance.

Table 1.

The correlation coefficients between IRF and other hematological parameters were calculated for each age group.

	<8 days	8–30 days	30 days–1 year		1–16 years	16–55 years	>55 years
Tests	IRF
WBC	0.635**	0.664**	0.339*	0.259**		0.207*	–0.083
Hb	0.257*	0.224*	–0.438**	–0.493**		–0.570**	–0.426**
RBC	0.124	0.116	–0.298*	–0.414**		–0.414**	–0.315**
HCT	0.340*	0.265**	–0.337*	–0.508**		–0.541**	–0.364**
MCV	0.500**	0.368**	0.141	–0.028		–0.040	0.121
MCHC	–0.261*	–0.161	–0.077	–0.195**		–0.346**	–0.314**
PLT	–0.126	–0.111	0.011	0.165*		0.105	0.044
%RET	0.864**	0.835**	0.547**	0.451**		0.647**	0.464**
CHR	0.306*	0.370**	–0.301*	–0.299**		–0.163	–0.244*
Neut count	0.640**	0.645**	0.286*	0.080		0.276*	–0.003
Neut%	0.741**	0.610**	–0.050	–0.056		0.190*	–0.033
Lenf count	–0.143	–0.013	0.156	0.222**		–0.096	–0.145
Lenf%	–0.768**	–0.546**	–0.068	0.026		–0.198*	0.045
Mon count	0.153	0.186	0.390*	0.173*		0.289*	–0.129
Mon%	–0.445**	–0.294**	0.314*	0.058		0.177	0.043
Eosi count	0.101	0.135	0.429*	0.198**		–0.151	–0.100
Eosi%	–0.325*	–0.244*	0.297*	0.088		–0.225*	–0.116
Baso count	0.226*	0.083	0.000	0.076		–0.115	–0.044
Baso%	–0.071	–0.114	–0.059	–0.089		–0.237*	–0.049
MPV	–0.442**	–0.268*	0.344*	–0.018		0.011	0.082
RDW	0.332*	0.303**	0.316*	0.531**		0.541**	0.540**

Spearman correlation analysis was performed to assess the relationship between IRF and hematological parameters across different age groups. Statistical significance is indicated by asterisks (*P < .05; **P < .01). Positive correlation coefficients reflect a direct association with higher IRF values, whereas negative coefficients indicate an inverse relationship between IRF and the corresponding hematological parameter.

Baso# = basophil count, Baso% = basophil percentage, CHR = reticulocyte hemoglobin content, Eos# = eosinophil count, Eos% = eosinophil percentage, G-CSF = granulocyte-colony stimulating factor, Hb = hemoglobin, HCT = hematocrit, HSC = hematopoietic stem cell, IRF = immature reticulocyte fraction, Lymph# = lymphocyte count, Lymph% = lymphocyte percentage, MCHC = mean corpuscular hemoglobin concentration, MCV = mean corpuscular volume, Mono# = monocyte count, Mono% = monocyte percentage, MPV = mean platelet volume, Neut# = neutrophil count, Neut% = neutrophil percentage, PLT = platelet count, RBC = red blood cell count, RDW = red cell distribution width, %RET = reticulocyte percentage, WBC = white blood cell count.

3.1. Gender-based differences

The study also analyzed IRF distribution by gender. IRF values did not differ significantly between males and females. The median (IQR) IRF was14.80 (9, 5–26, 18) in males and 14.10 (9.17–22.93) in females. (Wilcoxon–Mann–Whitney test, P > .05) These findings suggest that while minor differences exist, sex does not appear to be a major determinant of IRF distribution in healthy individuals.

The distribution of the median (IQR) IRF values across age groups revealed statistically significant and physiologically relevant patterns (Fig. 1).

Figure 1.

Age-based differences.

IRF demonstrated a pronounced and biologically coherent age-dependent decline. Neonates (<8 days) exhibited markedly elevated IRF levels, with a median of 38.60 (24.37–42.37), reflecting the heightened erythropoietic activity characteristic of the immediate postnatal period. A substantial reduction was observed in the 8- to 30-day group median 19.50 (13.30–30.43), and this downward trajectory persisted through the 30 days to 1 year interval median 15.90 (11.20–29.03). Children aged 1 to 16 years showed further attenuation median 10.90 (7.20–17.42), whereas adults (15–55 years) presented with comparatively stable and lower IRF distributions median 12.00 (8.53–19.30). A modest elevation was noted in individuals older than 55 years median 15.15 (10.69–23.51), potentially reflecting age-associated alterations in erythropoietic dynamics.

The Kruskal–Wallis test confirmed statistically significant heterogeneity across age groups (H = 198.82, df = 5, P < .001). Subsequent post hoc Wilcoxon–Mann–Whitney comparisons demonstrated that neonates differed significantly from all other groups (P < .001 underscoring the uniquely dynamic hematopoietic environment characteristic of the neonatal period.

The study analyzed the correlations between IRF and various hematological parameters across 6 distinct age groups (Table 1). The ensuing discussion will provide a synopsis of the findings, emphasizing the salient correlations and their clinical implications. The correlation coefficients between IRF and other hematological parameters were calculated for each age group.

A positive correlation was observed in neonates (age <8 days: 0.257), but this correlation became negative in older age groups (age >55 years: –0.426). This finding suggests a positive association between IRF and Hb in early life, indicative of active erythropoiesis. In contrast, the negative correlation observed in older adults may be indicative of impaired erythropoiesis due to age-related factors. A weak positive correlation was identified in neonates (age <8 days: 0.124), yet this correlation shifted to negative in older age groups (age >55 years: –0.315). This phenomenon is consistent with the decline in bone marrow activity that is typically associated with the aging process. A similar correlation was observed in neonates (age <8 days: r = 0.340), which became negative in older adults (age >55 years: r = –0.364). This finding lends further credence to the hypothesis of diminished erythropoietic activity in the elderly. A robust positive correlation was identified across all age groups (pediatric: 0.864; >55 years: 0.464). This finding suggests that IRF can serve as a reliable marker of reticulocyte production and bone marrow activity, irrespective of age. A positive correlation was observed across all age groups (pediatric: 0.332; geriatric: 0.540). This finding indicates that the combination of IRF and RDW may offer significant insights into the assessment of erythropoietic activity and red cell heterogeneity.

A strong positive correlation was observed in neonates (<8 days: 0.635) and infants (8–30 days: 0.664), but the correlation weakened in older age groups (>55 years: –0.083). This phenomenon is indicative of the active bone marrow response in early life, which undergoes a decline with age. Neut#; a positive correlation was observed in neonates (0.640) and infants (0.645), but the correlation was weak in older adults (P < .05). This finding suggests a close association between granulopoiesis and erythropoiesis during the early stages of life.

Lymph#; a weak or negative correlation was observed across all age groups, suggesting that lymphocytes are less influenced by erythropoietic activity.

Platelet count; a weak positive correlation was observed in certain age groups (1–16 years: 0.165), yet no significant correlation was identified in other groups. This finding indicates that platelet production is not significantly associated with IRF. Mean platelet volume a negative correlation was observed in neonates (<7 days: –0.442), but the correlation was weak or absent in older age groups. This phenomenon may be indicative of disparities in platelet production and turnover across different age demographics.

Figure 2 assesses the overall relationship between WBC and IRF independent of age stratification. A pooled correlation analysis was performed across all subjects (n = 853). A statistically significant moderate positive correlation was observed between IRF and WBC based on Spearman rank correlation analysis (r = 0.408, P < .001), indicating that higher WBC counts are moderately associated with increased IRF levels. This pooled evaluation provides an overall perspective before age-stratified analyses are considered.

Figure 2.

Linear regression analysis demonstrating the pooled correlation between IRF and WBC across all age groups. IRF = immature reticulocyte fraction, WBC = white blood cell count.

In Figure 3, the association between IRF and WBC is presented across 6 distinct age groups. Each subfigure includes the fitted linear regression line with its 95% confidence interval, together with the corresponding correlation coefficient (r) and P-value. A clear attenuation of the IRF–WBC relationship is observed with advancing age, indicating a pronounced age-dependent modulation of this association. <8 days (r = 0.63, P < .001); 8 to 30 days (r = 0.67, P < .001); 30 days to 1 year (r = 0.36, P = .006); 1 to 16 years (r = 0.23, P < .001); 16 to 55 years (r = 0.21, P = .024); and >55 years (r = 0.03, P = .737).

Figure 3.

Age-stratified associations between IRF and WBC across 6 age groups. IRF = immature reticulocyte fraction, WBC = white blood cell count.

A multiple linear regression analysis using the least squares method was performed. WBC, age, and hemoglobin were all identified as significant predictors of IRF. However, examination of the partial correlation coefficients demonstrated that the contributions of age and hemoglobin were markedly smaller compared with that of WBC (partial r = 0.0607 and 0.1278, respectively) (Table 2).

Table 2.

Multiple linear regression analysis evaluating the independent effects of WBC, age, and hemoglobin on IRF.

Independent variables	Coefficient	t	P	r partial
(Constant)	10.3840
WBC	1.4913	20.461	<.001	0.5628
Age	0.03242	2.208	.0275	0.0607
Hemoglobin	–0.5185	–4.648	<.001	0.1278

IRF = immature reticulocyte fraction, WBC = white blood cell count.

A multiple linear regression model was constructed to evaluate the independent associations of WBC, age, and hemoglobin with IRF (Fig. 4). The model yielded r = 0.5981 (R² = 0.3572). WBC, age, and hemoglobin were statistically significant predictors; however, partial correlation coefficients indicated that the contributions of age and hemoglobin were minimal compared with WBC.

Figure 4.

Multiple linear regression model illustrating the combined effect of WBC, age, and hemoglobin on IRF. IRF = immature reticulocyte fraction, WBC = white blood cell count.

4. Discussion

This study aimed to investigate the age-related physiological correlations between IRF and standard hematological parameters in a cohort of healthy individuals with no systemic, hematologic, or autoimmune disease. The findings demonstrate that IRF serves not only as a marker of erythropoietic activity but also as a sensitive indicator of functional changes in hematopoiesis across the human lifespan.^[7,9] The age-dependent variation in correlation patterns provides insight into the regulation of erythropoiesis under non-pathological conditions.

During the neonatal period, strong positive correlations were observed between IRF and total WBC count, neutrophil count, and %RET, reflecting synchronized activation of erythroid and myeloid lineages following birth.^[10,11] This early hematopoietic activation is driven by increased erythropoietin production in response to the sharp rise in oxygen demand postnatally,^[12] while granulocyte-colony stimulating factor contributes to neutrophil proliferation.^[13] The association between IRF and elevated mean corpuscular volume and RDW in neonates mirrors the morphological heterogeneity of immature erythrocytes, commonly observed during this developmental window.^[14]

As individuals transition from infancy to childhood, correlations between IRF and immune cell indices gradually diminish, while inverse correlations with Hb, HCT, and red blood cell count become more prominent.^[15,16] In the 1 to 16 year age group, the negative correlation between IRF and Hb (r ≈ –0.570) likely reflects a physiological erythropoietic response to increased metabolic demands associated with growth, even in the absence of clinical anemia.^[17] These findings align with studies demonstrating that childhood is a period of fluctuating iron requirements and dynamic red cell turnover.^[18]

In adulthood, IRF retains an inverse relationship with Hb and related indices, but correlations with WBC and neutrophils are notably weaker. This shift indicates that IRF increasingly reflects erythropoietic needs independent of myelopoiesis as hematopoietic regulation becomes more compartmentalized.^[19] The preserved correlation with %RET (r ≈ 0.64) supports the continued utility of IRF as an early marker of bone marrow response in this age group.^[20]

In elderly individuals, correlations between IRF and leukocyte parameters become negligible (r ≈ –0.08), while inverse correlations with hemoglobin persist (r ≈ –0.42), suggesting that IRF reflects preserved erythropoietic activity rather than immune cell output.^[21] These patterns are consistent with hematopoietic aging, characterized by a decline in stem cell proliferation and lineage bias toward myeloid cells, coupled with reduced responsiveness to physiological stress.^[22,23] Immunosenescence may also contribute to the decoupling of IRF from leukocyte parameters in advanced age.^[24]

A consistent and particularly meaningful finding was the positive correlation between IRF and RDW across all age groups, which became stronger with aging. RDW reflects anisocytosis and erythrocyte volume heterogeneity, both of which increase with age.^[25] The parallel rise of IRF and RDW in older adults suggests that IRF may track ineffective erythropoiesis or stress-induced red cell production in physiologically aging marrow.^[26]

The relationship between IRF and CHR was modest and negative in younger individuals, likely indicating higher iron demand during growth.^[27] The reduced association in older age suggests that IRF is more sensitive than CHR to early erythropoietic stimulation, as also reported in prior studies evaluating erythropoietin therapy response.^[28]

One of the key findings of this study is the age-dependent decline in the correlation between WBC and IRF. The strong positive correlation observed in neonates and infants indicates that both erythropoiesis and granulopoiesis are activated simultaneously during early life. This likely reflects the hematopoietic system’s adaptation to extrauterine stressors, such as hypoxia, oxidative stress, and immune priming.^[29,30]

As individuals age, however, this correlation progressively weakens and becomes negligible in adults. This change aligns with literature describing age-related compartmentalization within the bone marrow microenvironment and the declining responsiveness of hematopoietic stem and progenitor cells.^[31,32] Studies show that, with aging, the hematopoietic stem cell pool becomes less efficient and that both erythroid and myeloid outputs become more independently regulated.^[33] This explains the dissociation of IRF from WBC in older adults, reflecting physiological marrow remodeling and immunosenescence.

Overall, the age-dependent shift in IRF correlations with hematological parameters demonstrates how IRF can provide insight into physiological hematopoiesis throughout life. In neonates, IRF reflects global bone marrow activation. In childhood, IRF mirrors compensatory responses to increased oxygen demand. In adulthood, IRF remains a stable indicator of erythropoietic equilibrium. In elderly individuals, IRF may serve as a marker of preserved hematopoietic reserve. These findings underscore the importance of establishing age-specific reference intervals for interpreting IRF, highlighting its potential utility in clinical and research settings, particularly when combined with Hb, RDW, and CHR for the early detection of subclinical changes in erythropoietic activity.

4.1. Clinical implications and age-specific interpretation of IRF

The age-specific IRF–WBC relationship patterns demonstrated in this study directly contribute to the clinical interpretation of reticulocyte indices. In newborns, high IRF levels, even when accompanied by low or borderline WBC, mostly reflect physiological erythropoietic activity^[34]; increased erythropoiesis in the postnatal period should be considered part of normal hematopoietic adaptation rather than an indicator of bone marrow stress.^[34,35] In contrast, the same finding in adults carries a different meaning: the combination of high IRF and low WBC may be associated with increased compensatory erythropoiesis in early-stage anemia, the onset of bone marrow recovery after chemotherapy, or an acute stress erythropoiesis response.^[19,36] In the older age group, however, the IRF–WBC relationship is markedly weakened, and leukopenia accompanied by increased IRF should be considered a warning sign for subclinical bone marrow failure, the onset of myelodysplastic syndrome, B12/folate deficiency, or unexplained cytopenias.^[37,38] Therefore, the age-specific IRF–WBC assessment framework offers a practical tool that may increase diagnostic accuracy in anemia diagnosis algorithms, in monitoring bone marrow regeneration after chemotherapy, and particularly in the differential diagnosis of unexplained cytopenias in elderly patients.^[39]

4.2. Limitation

A fundamental limitation of this study is that participants’ health status was determined based on electronic medical records and laboratory data rather than a comprehensive clinical assessment. Therefore, the possibility that individuals with subclinical or undiagnosed conditions may have been inadvertently included in the study cannot be entirely ruled out.

5. Conclusion

In conclusion, this study highlights the clinical utility of IRF as a marker of erythropoietic activity and bone marrow function across different age groups. The age-related variations in correlations provide valuable insights into the dynamics of hematopoiesis and underscore the importance of considering age when interpreting IRF values in clinical practice. Future studies should explore the prognostic value of IRF in specific patient populations and investigate the causal relationships between IRF and other hematological parameters.

Author contributions

Conceptualization: Özlem Doğan, Muhittin A. Serdar.

Data curation: Özlem Doğan, Muhittin A. Serdar.

Formal analysis: Muhittin A. Serdar.

Methodology: Özlem Doğan, Muhittin A. Serdar.

Writing – review & editing: Özlem Doğan.