Anaemia remains one of the most pervasive global health challenges, disproportionately affecting populations in low- and middle-income countries.1,2 In developing nations such as India, the burden of anaemia continues to be substantial, with an estimated prevalence of approximately 42% among women aged 15–59 years, 30% among men in the same age group, and nearly 45% among adults above 60 years of age.3-5 These figures underscore the continuing public-health relevance of anaemia and its far-reaching impact on quality of life, productivity, and morbidity. Anaemia is broadly defined as a state in which the number of circulating red blood cells, and consequently the oxygen-carrying capacity of blood, are insufficient to meet the physiological requirements of the body. The World Health Organization (WHO) classifies anaemia into mild, moderate, and severe categories based on hemoglobin concentration, providing a standardized framework for diagnosis and clinical decision-making.6-8 From a diagnostic perspective, morphological classification offers valuable insight into the underlying aetiology. Based on red cell indices, anaemia can be categorized as microcytic hypochromic (characterized by low mean corpuscular volume [MCV] <80 fL and reduced mean corpuscular hemoglobin concentration [MCHC] <30 g/dL), normocytic normochromic (normal MCV 82–100 fL), or macrocytic (MCV >100 fL).9-11 The integration of morphological features with red cell indices forms the cornerstone of laboratory evaluation, guiding both etiological interpretation and therapeutic management. The advent of advanced automated hematology analyzers has revolutionized the evaluation of blood cell counts, enabling rapid and precise quantification of numerous red cell and platelet parameters. In recent years, platelet parameters such as platelet count, plateletcrit (PCT), mean platelet volume (MPV), and platelet distribution width (PDW) have attracted considerable interest as potential biomarkers of various hematological and systemic disorders.12,13 Platelets, beyond their hemostatic function, play a dynamic role in inflammation, oxidative stress, and marrow response—all of which may be altered in anemic conditions.14-16 Despite the extensive characterization of red cell parameters in anaemia, relatively limited research has explored their interrelationship with platelet parameters. Understanding this correlation could provide additional diagnostic and pathophysiological insights, as well as contribute to refining the subclassification of anaemia. Only few studies in the literature have showed a statistically significant inverse linear relationship between hemoglobin concentration and platelet count. Such study is important, as many anemic patients are associated with platelet disorders. The objective of this study is to evaluate the correlation of different RBC parameters and platelet parameters in anemic patients with moderate and severe anemia.

Study Objective The primary objective of this study was to assess the correlation between red blood cell (RBC) and platelet parameters in patients presenting with moderate and severe anaemia, as defined by the WHO. Study Design and Setting This was a retrospective observational study conducted in the Department of Pathology, Gian Sagar Medical College and Hospital, Rajpura, Punjab. The study period extended from January to December 2024. Ethical approval was obtained from the Institutional Ethics Committee prior to data retrieval and analysis. Study Population A total of 400 patients aged above 15 years who were diagnosed with moderate or severe anaemia based on WHO hemoglobin criteria were included in the study. Inclusion Criteria • Patients aged ≥15 years. • Diagnosed cases of moderate or severe anaemia according to WHO classification (moderate: Hb 8–10.9 g/dL; severe: Hb <8 g/dL). Exclusion Criteria • Pregnant women. • Patients with a recent history of blood transfusion (within the preceding three months). • Individuals with known platelet or leukocyte disorders. • Patients suffering from chronic hepatic, renal, or cardiac diseases. These exclusion criteria were applied to eliminate confounding factors that could independently influence red cell or platelet parameters. Data Collection and Laboratory Analysis Relevant hematological data were retrieved retrospectively from the hospital laboratory database. All blood samples had been analyzed as part of routine diagnostic evaluation using an automated three-part differential hematology analyzer (Nihon Kohden MEK-6420P, Japan). For each patient, the following red blood cell parameters were recorded: • Hemoglobin concentration (Hb, g/dL) • Red blood cell count (RBC, million/µL) • Hematocrit (Hct, %) • Mean corpuscular volume (MCV, fL) • Red cell distribution width (RDW, %) The corresponding platelet parameters included: • Platelet count (Plt, ×10³/µL) • Plateletcrit (PCT, %) • Mean platelet volume (MPV, fL) • Platelet distribution width (PDW, %) Patients were subsequently categorized into moderate and severe anaemia groups according to WHO guidelines based on hemoglobin concentration. Data Processing and Statistical Analysis All data were entered and organized in Microsoft Excel and subsequently exported for statistical analysis. Descriptive statistics, including mean and standard deviation (SD), were computed for all quantitative variables. Statistical analyses were performed using MedCalc Statistical Software, version 23.2.1 (MedCalc Software Ltd., Ostend, Belgium). The Pearson correlation coefficient (r) was calculated to determine the strength and direction of linear relationships between RBC indices and platelet parameters. Correlations were interpreted as positive or negative based on the sign of the coefficient. A p-value of <0.05 was considered statistically significant, while p <0.0001 was regarded as highly significant. Results were presented in tabular form with consolidated correlation matrices for both moderate and severe anaemia groups. Ethical Considerations This study utilized anonymized data obtained from institutional medical records. No direct patient contact or intervention was involved. All procedures were performed in accordance with the ethical standards of the institutional research committee and the 2013 revision of the Declaration of Helsinki.

The baseline hematological profile of the study cohort revealed a mean hemoglobin concentration of 8.4 ± 1.2 g/dL. The mean RBC count was 3.1 ± 0.7 million/µL, and the corresponding hematocrit averaged 28.3 ± 5.1 %, with MCV of 78.2 ± 9.4 fL and RDW of 16.8 ± 2.3 % indicate that the majority of cases displayed a microcytic picture with anisocytosis. Among platelet parameters, the mean platelet count was 280 ± 85 ×10³/µL, with a PCT of 0.23 ± 0.06 %, MPV (9.8 ± 1.1 fL) and PDW (15.7 ± 2.1 %). Collectively, these values establish a representative baseline of the hematologic characteristics in anemic patients from this tertiary-care cohort. There were 288 patients (72%) who exhibited normal platelet counts, with thrombocytopeniain 92 patients (23%). Only 20 patients (5%) presented with thrombocytosis. Comparison between moderate (n = 237) and severe (n = 163) anaemia groups revealed significant reductions in hemoglobin (p < 0.001), RBC count (p = 0.030), and hematocrit (p < 0.001) in severe cases, confirming the progressive decline in RBC parameters with increasing disease severity. However, MCV and RDW showed no statistically meaningful variation between the two groups (p = 0.620 and p = 0.450, respectively). Platelet parameters—including platelet count, PCT, MPV, and PDW—also did not differ significantly (all p > 0.4). Table 1: Comparison of Red Blood Cell and Platelet Indices Between Patients with Moderate and Severe Anemia Parameter Moderate Anemia (n=237) Severe Anemia (n=163) p-value RBC Indices Hemoglobin (g/dL) 9.13 ± 0.55 6.69 ± 1.04 < 0.001 RBC count (million/µL) 4.05 ± 0.77 3.37 ± 1.64 0.030 Hematocrit (HCT %) 30.48 ± 1.97 23.19 ± 3.67 < 0.001 MCV (fL) 77.15 ± 11.32 75.13 ± 16.14 0.620 RDW (%) 17.05 ± 2.93 17.95 ± 2.65 0.450 Platelet Indices Platelet count (x10³/µL) 243.43 ± 118 233.29 ± 124.82 0.750 PCT (%) 0.16 ± 0.07 0.43 ± 3.59 0.420 MPV (fL) 6.72 ± 0.82 6.79 ± 1.05 0.840 PDW (%) 17.54 ± 1.36 17.37 ± 1.73 0.780 Correlation analysis using Pearson’s coefficient revealed distinct interaction patterns between RBC and platelet parameters in both anemia categories. In moderate anemia, strong positive correlations were observed between RBC and platelet count (r = 0.351, p < 0.0001), RBC and PCT (r = 0.370, p < 0.0001), and MCV with PDW (r = 0.509, p < 0.0001), whereas negative correlations existed between RBC and PDW (r = –0.536, p < 0.0001) and MCV with platelet count (r = –0.301, p < 0.0001). In severe anemia, PDW demonstrated the most consistent associations, showing significant negative correlation with RBC (r = –0.334) and Hct (r = –0.367), and a strong positive correlation with MCV (r = 0.662), all with p < 0.0001. These observations suggest that PDW, reflecting platelet anisocytosis, dynamically varies with changes in red cell morphology and marrow activity, especially in advanced anemia. Table 2: Correlation between Red Blood Cell and Platelet Parameters in Moderate and Severe Anemia Groups (Pearson's Correlation Coefficient) Pearson correlation Coefficient Matrix Parameter Analysed Moderate Anemia n=237 Severe Anemia n=163 Pearson’s coefficient value (PC) p value Pearson’s coefficient value (PC) p value Hb with Plt -0.104 0.1118 +0.015 0.8534 Hb with Pct -0.090 0.1676 -0.007 0.9312 Hb with MPV +0.093 0.1537 +0.090 0.2541 Hb with PDW +0.024 0.7101 -0.134 0.0877 RBC with Plt +0.351 <0.0001 0.167 0.0333 RBC with Pct +0.370 <0.0001 -0.046 0.5572 RBC with MPV -0.008 0.9020 -0.004 0.9624 RBC with PDW -0.536 <0.0001 -0.334 <0.0001 Hct with Plt +0.119 0.0671 +0.174 0.0262 Hct with Pct +0.152 0.0192 -0.013 0.8665 Hct with MPV +0.101 0.1222 +0.074 0.3474 Hct with PDW -0.191 0.0032 -0.367 <0.0001 MCV with Plt -0.301 <0.0001 -0.435 <0.0001 MCV with Pct -0.339 <0.0001 +0.092 +0.2446 MCVwith MPV +0.019 0.7722 +0.107 +0.1755 MCVwith PDW +0.509 <0.0001 +0.662 <0.0001 RDW with Plt +0.061 0.3514 +0.055 0.4885 RDW with Pct +0.078 0.2298 -0.069 0.3812 RDWwith MPV -0.022 0.7392 -0.009 0.9056 RDWwithPDW -0.354 <0.0001 -0.341 <0.0001 When the entire cohort was analyzed collectively, significant positive correlations emerged between RBC count and platelet count (r = 0.23), and between MCV and PDW (r = 0.61), both of which achieved statistical significance. Conversely, RBC and PDW demonstrated a marked negative association (r = –0.36), indicating that patients with lower erythrocyte counts tended to have greater platelet size variability. Similarly, MCV and MCH showed inverse correlations with platelet count, implying that smaller red cells (microcytosis) were often accompanied by reactive thrombocytosis. No meaningful correlations were detected between hemoglobin or MCHC and any platelet parameter. Table 3: Consolidated Correlation Analysis between Red Blood Cell and Platelet Parameters for the Entire Cohort (N=400) Platelet Parameters RBC Parameters Platelet count PCT MPV PDW r value Statistical Significant r value Statistical Significant r value Statistical Significant r value Statistical Significant RBC 0.23 YES -0.05 NO -0.01 NO -0.36 YES HB 0.01 NO -0.05 NO 0.02 NO 0.02 NO PCV 0.12 YES -0.05 NO 0.02 NO -0.13 YES MCV -0.39 YES 0.06 NO 0.06 NO 0.61 YES MCH -0.40 YES 0.04 NO 0.04 NO 0.62 YES MCHC -0.05 NO -0.01 NO 0.02 NO 0.07 NO RDW 0.05 NO -0.03 NO -0.01 NO -0.35 YES

This retrospective analysis of 400 patients with moderate to severe anaemia was designed to explore the interrelationship between RBC and platelet parameters—an area that often receives less attention in routine hematological evaluation. The findings of this study reveal that red cell and platelet indices share a dynamic, interdependent physiological relationship, reflecting a coordinated bone marrow response to hematopoietic stress. When interpreted in conjunction with previous research, these observations affirm established hematological concepts while unveiling patterns that refine our understanding of marrow behavior under anemic conditions. The baseline hematological profile of our cohort, reflects a population predominated by microcytic hypochromic (moderate) anaemia, with a mean hemoglobin of 8.4 g/dL, mean corpuscular volume (MCV) of 78.2 fL, and elevated red cell distribution width (RDW) of 16.8%. This morphological pattern usually is seen with iron deficiency anaemia (IDA) as the leading etiology. Kumari et al17 reported a microcytic hypochromic pattern in 69.3% of cases, Shetty et al18 in 67.1%, Vedashree et al19 in 57.07%, and Anand et al20 in 48%. However, as highlighted by Anand et al20 (28% dimorphic) and Garg et al and others21,22, the coexistence of mixed deficiencies can obscure straightforward morphological classification, particularly in cases where dimorphic anaemia or nutritional overlap is present. The platelet count distribution reveals that 72% of patients maintained normal platelet counts, mirroring the trends reported by Jundi et al23 (81.25%) and Kiran et al24 (66%). Nevertheless, thrombocytopenia was seen in 23% of our cohort, and thrombocytosis in 5%. The occurrence of reactive thrombocytosis in anaemic patients is a well-established compensatory response, particularly in iron deficiency states as documented in many other studies.25-27Conversely, Kiran et al24 noted that thrombocytosis also appeared in normocytic and macrocytic anaemias, suggesting that this response may represent a nonspecific marrow adaptation to hypoxia rather than an isolated feature of IDA. The comparative analysis of red cell and platelet indices (Table 1) provides a crucial insight: the severity of anaemia (moderate vs. severe) did not significantly influence platelet parameters. While hemoglobin, RBC count, and hematocrit declined significantly with increasing severity, the platelet count, PCT, MPV, and PDW remained statistically unchanged. This observation helps reconcile the variability reported in earlier works. For instance, Anand et al20 studied predominantly severe anaemia (80%) but noted substantial heterogeneity in platelet indices owing to diverse underlying etiologies. Similarly, Prasanna et al30 identified normocytic normochromic anaemia (53.4%)—often secondary to chronic disease—as the most prevalent type among men, a condition that affects platelet dynamics differently from IDA. Hence behaviour of platelets may be impacted more by etiology rather than the severity of anemia alone. Our correlation analysis (Tables 2 and 3) revealed robust and biologically meaningful relationships between red cell and platelet indices. The most consistent finding was the negative correlation between MCV/MCH and platelet count. This inverse relationship confirmed in our data and echoed in the works of Kiran et al24, Jundi et al23, and Deepak Kumar et al31, reflects a bone marrow milieu where iron deficiency hinders erythropoiesis while simultaneously promoting or permitting reactive thrombocytosis. Shetty et al18 also demonstrated elevated mean platelet counts and MPV/platelet ratios in microcytic hypochromic anaemia, reinforcing this compensatory trend. A particularly striking and novel observation in our study was the behavior of Platelet Distribution Width (PDW), which exhibited the strongest and most consistent correlations with red cell indices. PDW showed a significant positive correlation with MCV (r = 0.509 in moderate anaemia and r = 0.662 in severe anaemia) and a negative correlation with RBC count and hematocrit, suggesting that PDW acts as a sensitive indicator of marrow dysregulation. Since PDW reflects platelet size heterogeneity, its elevation likely mirrors an asynchronous marrow response to hypoxic stress or nutrient deficiency. Conceptually, PDW may serve as a counterpart to RDW, signifying heterogeneity in platelet production akin to anisocytosis in red cells. This interpretation expands upon Deepak Kumar et al31 finding of a direct relationship between RDW and platelet count, by pinpointing PDW as a more discriminating marker of marrow activity. The strong positive association between MCV and PDW in our primarily microcytic cohort could represent subtle dyshematopoietic processes or concurrent nutritional deficits. Methodologically, our use of automated hematology analyzers, as in other studies, underscores the power of modern hematological automation in quantifying subtle inter-parameter relationships. However, as Garg et al21 cautioned, analyzer-based data must complement—not replace—the morphological assessment of peripheral smears, especially for identifying dimorphic or mixed-pattern anaemias where automated interpretation may falter. Thus, while our correlations provide meaningful quantitative insight, definitive diagnostic evaluation should always integrate morphological examination. This study reinforces the concept that the bone marrow’s response to anemia is an integrated, interlinked process involving both erythroid and megakaryocytic lineages. While the distinctive and consistent behavior of PDW emerges as a promising, underutilized indicator of marrow stress and dyspoiesis. By synthesizing our results with an extensive body of prior literature, this study advances a more comprehensive understanding of anemia—one that emphasizes the complete blood count as a holistic reflection of marrow function, rather than a mere aggregation of isolated numerical values. Strength and Limitations The principal strength of this study lies in its relatively large sample size and the use of standardized, automated hematology analyzers, which ensured accuracy, reproducibility, and objective quantification of both red cell and platelet parameters. By evaluating multiple parameters across moderate and severe grades of anemia, the study provides a comprehensive and comparative perspective on marrow response dynamics. However, certain limitations must be acknowledged. Being retrospective in design, the study lacked control over potential confounders and did not include confirmatory biochemical markers such as serum iron, ferritin, or vitamin B12 levels. Moreover, as a single-center analysis, the findings may not be fully generalizable to broader populations.

The present study demonstrates that red blood cell and platelet parameters are interrelated components of the hematopoietic response to anemia, reflecting the integrated function of the bone marrow. Significant negative correlations between MCV and platelet count, along with strong positive associations between MCV and PDW, underscore that platelet indices—particularly PDW—can serve as valuable adjuncts in understanding the pathophysiological spectrum of anemia. While hemoglobin and RBC indices reliably gauge anemia severity, platelet indices reveal underlying marrow activity and compensatory mechanisms. Thus, the study highlights the importance of evaluating red cell and platelet indices in tandem, providing a more holistic picture of marrow function in anemic states.

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