European Journal of Cardiovascular Medicine

Diabetes mellitus is a chronic and severe metabolic disease of the modern period, which is associated with permanent hyperglycaemia caused by the malfunction of insulin secretion, insulin activity, or both [1]. It is a significant morbidity and mortality condition globally because of the long-term vascular risks that the condition poses to the kidneys, eyes, nerves, and the heart [2]. Diabetes pathological mechanisms result in microvascular and macrovascular damages, and diabetic nephropathy, retinopathy, and atherosclerotic vascular disease are some of the critical factors determining the prognosis of a patient [3]. Although there is a lot of research going on, predicting and preventing these complications in its early stages is not easy, and a constant search of easier biomarkers that may help in the early detection is done [4].

Red Cell Distribution Width (RDW) is one of the recent Hematological parameters that have sparked a considerable amount of interest as a potential biomarker of systemic inflammation and vascular damage based on the Hematological parameters yielded by regular complete blood counts [5]. RDW indicates the difference in size of red blood cell (RBC) or anisocytosis as it is commonly referred to and has been traditional in the classification of anaemia types. Nevertheless, there is rising evidence that high RDW is strongly associated with cardiovascular disease, kidney impairment, and comorbidities of diabetes despite the lack of anaemia [6]. Such a change in

perception implies that RDW is not a hematologic parameter alone, but also a predictor of physiological dysfunction like oxidative stress and chronic inflammation and defective erythropoiesis [7].

It is believed that progressive increase in RDW of diabetic patients reflects systemic metabolic stress, which is a cause of endothelial dysfunction and poor microcirculatory flow. Thus, measurement of RDW can give an indirect but significant information concerning the risk of diabetic nephropathy, retinopathy, and vascular dysfunction development [8].

• Burden of Diabetic Microvascular and Macrovascular Complications

The global increase in diabetes has led to a parallel increase in its own vascular complications, which imposes useful clinical and economic responsibility [9]. About half of all recent cases of chronic kidney failure are caused by diabetic kidney disease (DN), which is the leading cause of end-stage renal disease (ESRD) globally [10]. Similarly, among working-age individuals, diabetic retinopathy (DR) remains one of the leading causes of avoidable blindness. In diabetic societies, macrovascular complications such as peripheral artery disease, cerebrovascular stroke, and coronary artery disease continue to be the leading cause of mortality.

Chronic hyperglycaemia, protein glycation, lipid peroxidation, oxidative stress, and inflammatory cytokine activation are diabetes-related complications [11]. These complications are directly related to endothelial damage, vascular stiffness, and progressive organ damage. It is often difficult to detect subclinical microvascular damage, which requires timely, measurable, and low-cost index. The red blood cell (RDW) is a power indicator, as usual in a clinical laboratory. Encourage detection of RDW in individuals with poor glycaemic management and microalbuminuria, alternatively retinopathy, and propose a role in the progression of the disease.

• Pathophysiological Links Between RDW and Vascular Dysfunction

A few similar pathophysiological mechanisms may explain the connection of RDW to diabetic vascular complications. Oxidative stress, which damages the erythropoietin membrane and alters red blood cell deformability, is a primary driver of increased size variation and increased RDW principles. [12]. Inflammation additionally disrupts erythropoiesis by suppressing bone marrow responses and shortening red blood cell life, while proinflammatory cytokine release is preferentially TNF-, IL-1, and IL-6, which interferes with iron metamorphosis and erythropoietin function [13].

Nephritic function findings in erythropoietin deficiency and increased metabolic toxin in diabetic kidney disease, which impairs RBC generation and increases anisocytosis [14]. Microvascular damage caused by oxidative stress and inflammation contributes to a reduction in oxygen delivery to the retinal tissues, which may also affect erythropoietin morphology [15]. In addition, RDW increases correlate with endothelial dysfunction, atherosclerosis, and arterial stiffness, which is a prerequisite for diabetic vasculopathy in macrovascular complications.

Therefore, RDW should be watched as a replacement indicator of systemic vascular injury rather than a specific disease marker [16]. The principles promoting RDW reflect the combined effects of metabolic stress, chronic inflammation, oxidative imbalance, and impaired hematologic homeostasis, all hallmarks of diabetic microvascular and macrovascular pathology [17]. Several clinical trials have shown that higher RDW levels are independently associated with an increased risk of cardiovascular events and mortality in diabetic patients, confirming their predictive value [18].

• Clinical Relevance and Diagnostic Perspective

RDW's clinical utility lies in its portability, cost-effectiveness, and predictive value. As RDW is routinely generated by an Automated Blood Analyzer, it can be used as a readily available biomarker to measure vascular threat in diabetic patients who do not require further testing at an alternative cost [19]. Their participation in a standard diabetic assessment panel could provide clinicians with a rapid, integrated assessment of the inflammatory and oxidative environment, which would contribute to the development of complications [20].

The principles of the RDW have been integrated with the increased duration of diabetes, lack of glycaemic control, dyslipidaemia, and the presence of both microvascular and macrovascular complications. [21]. Consequently, regular monitoring of the RDW could facilitate the untimely designation of the wrong patient and motivate timely assistance through intensified glycaemic restraint, lifestyle modification, and anti-inflammatory or antioxidant therapy [22].

RDW, a biomarker associated with organ specific complications, could be used as a proxy for existing diagnostic instruments. It may indicate worsened renal function in diabetic kidney disease, parallel disease in diabetic retinopathy, and systemic endothelial damage in vascular dysfunction [23]. RDW promises to improve vascular liability stratification and coaching approaches to diabetes management. More research is needed to initiate standard cut-off beliefs and integrate RDW with other biochemical indicators [24].

2. Review of Literature
   • Global Burden of Diabetic Complications and the Role of Hematological Indicators

The residue of diabetes mellitus is one of the most pressing global health obstacles contributing significantly to morbidity and mortality. It may be classified as chronic hyperglycaemia, which has been associated with widespread vascular damage and metabolic disturbances. The increasing incidence of diabetes worldwide has therefore increased the prevalence of its chronic complications, including nephropathies, retinopathy, and vascular disorders. [25]. The costs of such complications include significant disabilities, medical assistance costs, and reduced well-being.

Oxidative stress, endothelial damage, and chronic inflammation are principally responsible for simultaneously causing microvascular and macrovascular damage in persistent hyperglycaemia. Despite frequently failing to detect untimely vascular changes, classic biomarkers such as fast plasma glucose, glycated haemoglobin, and serum creatinine are useful in monitoring diseases [26]. Hence, the determination of more hematologic indicators which are cheap, reproducible, and capable of mirroring systemic inflammation should be of increasing priority.

Red Cell allocation Width (RDW), a commonly reported parameter in the complete blood count, has emerged as a potential stand-in biomarker for measuring vascular health in diabetes. [27]. Red blood cell variability is reflected by the RDW and reflects the perturbation of erythropoiesis, iron metamorphosis, and oxidative stability. promote RDW standards indicate anisocytosis, a pronouncement influenced by oxidative stress and inflammatory cytokine. As diabetes is associated with both inflammation and erythropoietin damage, RDW provides an indirect but sensitive indicator of vascular impairment [28].

• Association of RDW with Microvascular Complications

Microvascular complications of diabetes, including nephropathies and retinopathy, are growing more rapidly as hyperglycaemia damages the endothelial layer of the small blood vessels. The current is heading for a thickening of the capillary wall, loss of pericytes, and tissue ischemia. The Red Cell Dispersion Width has been established to correlate with the pathological changes, as oxidative injury and chronic inflammation contribute to irregular erythropoiesis and cell size variation [29]

Increased renal function and microalbuminuria are associated with increased renal function in diabetic nephropathy. Inflammatory conditions in nephropathy contribute to erythropoietin resistance and nutritional imbalance simultaneously with the addition of red cell heterogeneity. Similarly, in diabetic retinopathy, superior RDW tiers have been detected in patients alongside excessive advanced retinal transformations, suggesting the role of hypoxia and microvascular stress in the progression of the disease [30].

Hence parallel biochemical and systematic changes within the microvasculature, demonstrating endothelial dysfunction and subclinical inflammation [31]. As it is possible to measure easily and at minimal cost, the RDW is a practical parameter for recognizing individuals during periods of nephropathies or retinopathy, even before clinical symptoms appear. The present predictive possibility enhances the value of screening and risk stratification for diabetic societies [32].

• Pathophysiological Mechanisms Linking RDW and Vascular Dysfunction

The association of promoting RDW and vascular dysfunction in diabetes may be multifactorial, including oxidative stress, inflammation, and decreased red cell homeostasis. Continuous hyperglycaemia contributes to the development of reactive oxygen species which damage the erythropoietin membrane and shortens its life span. The release of immature red cells with variable size guides to add to RDW [33].

"An inflammatory cytokine, like interleukin-6 and tumor necrosis factor-alpha", interferes with iron metamorphosis and reduces erythropoietin activity, thus contributing to anisocytosis. Such pathological changes have an adverse effect on microcirculatory current, on oxygen transport, and on vascular stiffness. Such hemodynamic disturbances contribute to the development of diabetic vasculopathy and to complications such as nephropathies and retinopathy [34].

A growing indication also implies that a combined index including RDW, also known as the RDW-to-Alkum or haemoglobin-to-RDW ratio, improves the predictive accuracy of vascular disorders. These ratios integrate hematologic and biochemical parameters, providing an excessive overall picture of inflammation and metabolic imbalance. Therefore, RDW acts as an integrative marker displaying oxidative injury, endothelial stress, and systemic inflammation which underlie diabetic vascular disease [35].

• Clinical and Diagnostic Implications

RDW's clinical relevance lies in its portability, reproducibility, and strong association with diabetic complications. It is routinely available as part of a complete blood count trial and can be easily integrated into a diabetes monitoring program. The principles promoting RDW are systematically linked to underprivileged glycaemic control, long disease duration, and greater risk of vascular events [36]. The monitoring of RDW may thus help identify patients at greater risk of nephropathy, retinopathy, and cardiac dysfunction, if treatment has been previously curative.

RDW has been associated with increased hospitalisation rates and mortality in diabetic patients under the predictive provisions. The association of RDW with oxidative stress and endothelial dysfunction suggests that RDW reflects vascular health in general. Continuous monitoring of the principles governing the RDWs could assist clinicians to personalize treatment methods and enhance the long-term effects.

However, RDW should not remain interpreted separately. Numerous confusing components, such as anaemia, iron deficiency, and chronic inflammation not related to diabetes, affect this. Therefore, to ensure accuracy, its diagnostic use must remain integrated with other laboratory parameters. [37]. Despite the limitations mentioned above, RDW is a promising additional marker for the timely detection and management of diabetic complications.

A cost-effective and widely applicable strategy to measure vascular damage can be provided by integrating RDW into diabetic hazard appraisal frameworks. RDW has the potential to develop into a necessary biomarker in the everyday clinical assessment of diabetic patients, supporting timely involvement and reducing the burden of microvascular and macrovascular complications [38].

The current study adopted a prospective case–control experimental design aimed at measuring the association between red cell diameter (RDW) and diabetic microvascular and macrovascular complications. The main objective was to assess whether RDW could serve as a replacement biomarker for diabetic kidney disease, retinopathy, as well as vascular impairment in individuals with type 2 diabetes (T2DM). The study was conducted in the Department of Pathology in Indore, Madhya Pradesh, India, in collaboration with the Department of Medicine, Mahatma Gandhi Medical College, and Maharaja Yeshwant Rao Holkar Hospital. Study Setting and Duration The research was conducted jointly with the Department of Pathology and Medicine, M.Y. The Hospital of Indore, which is functioning as a 3rd-level teaching hospital and provides medical care to a large patient population in Indore and surrounding areas. the duration of the investigation shall be increased to over 18 calendar months (January 2023 – June 2024), i.e. a suitable duration for the continued recruitment, hematologic and biochemical testing, as well as statistical examination. Study Population During the research period, a community dweller diagnosed with Type 2 diabetes Mellitus (T2DM), age 18 months and older, regardless of their identity, visits the outpatient and inpatient departments of the hospital. At the same time, diabetic patients who are free of complications were included, together with those who were in good health without diabetes. Inclusion Criteria "Male and female patients diagnosed with Type 2 Diabetes Mellitus (T2DM)" with or without complications. Age ≥18 years. Patients and controls willing to provide written informed consent. Apparently healthy individuals as controls. Exclusion Criteria Diabetes other than Type 2 (e.g., Type 1 diabetes, gestational diabetes). Type 2 diabetic patients with comorbidities likely to alter RDW, such as: Hematological disorders Liver disease Malignancy or cancer HIV/AIDS Individuals with acute infections, chronic inflammatory diseases, or on erythropoietin therapy. Pregnant women or those with systemic illnesses affecting erythropoiesis. Sample Size Determination A total of 160 subjects were included in the study, comprising: 50 diabetic patients without complications, 65 diabetic patients with complications, and 45 healthy non-diabetic controls. The sample size was determined using the statistical formula: n=(Z^2 pq)/d^2 Where: Z=1.96(for 95% confidence level), p=17.3(prevalence of Type 2 diabetes in previous studies), q=1-p=82.7, d=10%(margin of error). By calculation, n=55; to ensure robustness, 160 participants were included in total. Data Collection Procedure Data were collected through a structured proforma, which included demographic, clinical, biochemical, and Hematological parameters. Clinical data: age, gender, duration of diabetes, BMI, blood pressure, and medication history. Biochemical data: "fasting blood glucose (FBG), HbA1c, lipid profile, serum creatinine", and urine microalbumin. Hematological data: complete blood count (CBC) including RDW, haemoglobin, total leucocyte count, platelet count, and mean corpuscular indices. Diagnostic data: fundoscopic examination for diabetic retinopathy, urine albumin-to-creatinine ratio (ACR) for nephropathy, and Doppler or ABI measurement for vascular dysfunction. Laboratory Analysis Hematological Parameters A venous blood sample was collected in an EDTA tube for a CBC examination. The RDW and other hematologic indicators were measured using an automated haematology analyser, e.g., Sysmex XN-1000. RDW-CV (%) was calculated automatically as: "RDW-CV"="Standard deviation of RBC volume" /"Mean corpuscular volume" ×100 Reference range: 11.5–14.5%. Biochemical Parameters Glucose, HbA1c, serum creatinine, and lipid profile were analyzed in a fast blood sample using Beckman Coulter AU480. Microalbuminuria was detected using immunoturbidimetric methods and expressed as an albumin/creatinine ratio (mg/g). Diabetic Retinopathy Assessment Ophthalmological evaluation was transferred using fundoscopy and optical coherence imaging (OCT) when necessary. Retinopathy was assessed according to the late medication Diabetic Retinopathy Exam (ETDRS) classification. Diabetic Nephropathy Assessment Kidney function was assessed via eGFR (CKD-EPI equation) and microalbuminuria. Patients were grouped into: "Normoalbuminuria (<30 mg/g)" "Microalbuminuria (30–300 mg/g)" "Macroalbuminuria (>300 mg/g)" Vascular Dysfunction Assessment The peripheral vascular function was measured using the Ankle-Brachial Index (ABI) and Doppler ultrasound of the lower limb arteries. ABI 0.9 was deemed to indicate peripheral atherosclerosis. Grouping of Study Subjects Participants were categorized into three subgroups based on the presence of complications: Group I: Diabetic patients without complications Group II: Diabetic patients with microvascular complications (nephropathy and/or retinopathy) Group III: Patients with diabetes who have macrovascular problems (such as ischemic heart disease or peripheral vascular disease) A control group of healthy, non-diabetic individuals was also included for comparison. Statistical Analysis Microsoft Excel 2019 and SPSS version 26.0 were used to analyze the data. Categorical variables were represented as percentages, and continuous components as mean SD. Pearson's relationships between RDW and clinical indicators were measured using the t-test and ANOVA for group comparisons. RDW was shown to be an independent predictor of diabetes complications using the Multivariate Logistic Arrest Development, and p 0.05 was deemed statistically significant. Ethical Considerations The Institutional Ethics Committee of the Mahatma Gandhi Medical College and M.Y. Hospital, Indore, blessing number: EC/MGM/Aug-22/35. Prior to registration, all players were granted a transcribed expert authorization. All the way through the intrigue, privacy, and anonymity were maintained. Each protocol shall comply with the standards set out in the Helsinki Statement 2013 concerning virtuous investigations, including human participants.

• Baseline Characteristics of Study Participants.

One hundred and sixty individuals (N=160) took part and were analyzed consisting of 50 non-complicated diabetic patients (Group I), 65 patients with complications (Group II), and 45 healthy non-diabetic controls (Group III).

Respondents' average age was 53.4 years; SD 9.8, and there was no significant difference between the two groups' average ages (p = 0.128). The sex proportions were 1.2:1 (that is, a mild degree of male dominance). The means of diabetes were 8.6 + 4.2 years of diabetes in diabetic individuals.

Table 1. Demographic and Clinical Profile of Study Participants

Interpretation: Table 1 presents the comparison of the demographic and clinical attributes of the study groups. The age and gender distribution of the participants were close and therefore indicate well-matched cohorts. But diabetic patients with complications reported a much longer time of having diabetes (9.5 ± 4.7 years) than patients without complications (6.4 ± 2.8 years) indicating that there is a strong correlation between the time of having the disease and the risk of complications. Besides, the complication group also had significantly higher both BMI and systolic blood pressure, the indicators of worse metabolic control and more cardiovascular effort. These conclusions emphasize that the long period of diabetes, being overweight of the body weight, and the high level of blood pressure are major clinical determinants of the development and degree of diabetic complications.

• Biochemical and Hematological Parameters

As expected, diabetic patients had considerably higher mean fasting blood glucose (FBG) and HbA1c than controls (p < 0.001). The complication group's mean blood creatinine and urine albumin-to-creatinine ratio (ACR) were noticeably higher, suggesting early renal system impairment.

Red Cell Distribution Width (RDW-CV) had a statistically significant progressive increase between the control, diabetic patients with complications (12.7 ± 0.8% vs 13.9 ± 1.2% vs 15.3 ± 1.6; p < 0.001).

Table 2. Comparison of Biochemical and Hematological Parameters

Interpretation: Table 2 provides the comparison of the biochemical and hematologic parameters of the controls and diabetic groups. As a marker of inadequate glycemic control, the levels of fasting blood glucose (FBG) and hemoglobin A1c were significantly elevated in individuals with diabetes, particularly in those who had complications (p < 0.001). Renal damage indicators such as albumin-to-creatinine ratio (ACR) and serum creatinine were also considerably higher in the group that had complications. Patients with diabetes who had complications exhibited a significantly wider red cell distribution width (RDW-CV) compared to controls (p < 0.001), indicating that this width is linked to the severity of their illness. The only significant difference was the slight decrease in the hemoglobin levels of diabetics, but there was no notable difference in the number of platelets. In general, these results validate the deterioration of metabolic and hematological conditions in patients with complications and diabetes.

• Association Between RDW and Diabetic Complications

RDW significantly increased in diabetic patients when further subdivided according to complication type with nephropathy and retinopathy having a higher RDW score than uncomplicated diabetes (p < 0.001).

Macrovascular compromise (i.e. peripheral vascular disease and ischemic heart disease) patients showed the greatest mean RDW (15.8 ± 1.4%).

Table 3. RDW Across Diabetic Complication Subgroups

Interpretation: Table 3 shows comparison of red cell distribution width (RDW) in various diabetic complication subgroups. Values of RDW were very significant among all diabetic populations than healthy controls (p < 0.001). The patients who had the highest RDW levels were patients with macrovascular disease (15.8 ± 1.4%), then, patients with nephropathy, and patients with retinopathy. This gradual rise in RDW between uncomplicated diabetes, microvascular and macrovascular complications indicates that high RDW indicates increased systemic inflammation, oxidative stress and endothelial dysfunction. The results suggest that RDW can be used as a convenient, cost-effective biomarker that can be used to detect diabetic patients with a greater risk of developing vascular and organ-specific issues.

Graph 1: RDW (%) Mean ± SD

Interpretation: Graph 1 depicts how the Red Cell Distribution Width (RDW %) in the various study groups varies. The control group had the least RDW value of (12.7), which represents normal variation in the size of erythrocytes. Conversely, Type 2 Diabetes Mellitus (T2DM) patients showed a progressive growth in the levels of RDW, and T2DM without complications a medium rise (13.9%). Importantly, the level of RDW in patients with nephropathy due to diabetes (15.2% and 15.0, respectively) and retinopathy (15.8% and 15.8% respectively) was the highest and was the highest among patients with several complications (15.8% and 15.8%, respectively). The trend indicates that high RDW is linked to the existence and intensity of diabetic complications, which may be due to high levels of oxidative stress, inflammation, and defects in erythropoiesis in the late stages of diabetes.

• Correlation Analysis

Correlation coefficient Pearson correlation showed a positive significant correlation in RDW and both HbA1c (r = 0.61, p < 0.001) and urine ACR (r = 0.57, p < 0.001).

"The negative correlation was observed with the estimation of the glomerular filtration rate eGFR (r = -0.48, p < 0.01)".

Table 4. Correlation of RDW With Key Clinical and Biochemical Variables

Interpretation: Table 4 shows that there is a substantial association between Red Cell Distribution Width (RDW) and critical clinical and biochemical factors in type 2 diabetes. A significant correlation between HbA1c and the variability of red blood cell size as measured by RDW (r = +0.61, p < 0.001) indicates that there is a correlation between inadequate glycemic management and a larger variability in red blood cell size. A significant connection with the albumin-to-creatinine ratio (ACR) (r = +0.57, p < 0.001) further demonstrated its association with renal microvascular injury. The inverse relationship between RDW and eGFR (r=-0.48, p=0.008) demonstrates that renal impairment improves eGFR. Also, RDW had a good correlation with diabetes duration (r = +0.42, p = 0.012), which means it might be used to anticipate how the illness would proceed and its potential effects.

Graph 2: RDW Correlation with Clinical Variables

Interpretation: In diabetic patients, the graph 2 shows the correlation of Red Cell Distribution Width (RDW) with some important clinical and biochemical variables. There is a significant correlation between RDW and HbA1c (r = +0.61, p < 0.001) and the albumin-to-creatinine ratio (ACR) (r ≈ +0.57, p < 0.001), suggesting that poorer glycaemic management and renal impairment are associated with an increase in RDW. In contrast, there is a negative correlation between RDW and estimated glomerular filtration rate (eGFR) (r ≈ -0.48, p = 0.008), suggesting that decreased kidney function is linked to high RDW. Also, the time of diabetes is positively correlated with RDW with moderate levels (r ≈ +0.42, p = 0.012), which states that RDW has the tendency to rise with the length of the disease. Overall, RDW seems to be a possible indicator that can describe glycemic and renal dysfunction in diabetes.

• Logistic Regression Analysis

Multivariate logistic regression showed that RDW of 14.5% and above was a predictor of diabetic complications (adjusted OR = 3.26, 95% CI = 1.686.32, p = 0.001) even after controlling HbA1c, period of diabetes and serum creatinine.

Table 5. Logistic Regression Showing RDW as Predictor of Diabetic Complications

Interpretation: Table 5 shows the logistic regression analysis that considers RDW as a predictor of diabetic complications. RDW 14.5% and above had been significantly related to the risk of complications, The adjusted odds ratio (OR) for patients with significant RDW was 3.26 (95% CI: 1.686.32, p = 0.001), indicating a threefold increased risk of problems. There was a strong correlation between HbA1c and the likelihood of complications, suggesting that poor glycemic management increases the likelihood of these problems (OR = 1.52, 95% CI: 1.132.04, p = 0.009). Additionally, the duration of diabetes (OR = 1.27, 95 percent CI: 1.061.52, p = 0.015) and higher serum creatinine levels (OR = 1.81, 95 percent CI: 1.122.91, p = 0.019) were also significant predictors, suggesting their involvement in the disease's development. In general, high RDW is a predictive factor of diabetic complications.

Graph 3: Clinical and Predictive Value of RDW in Diabetes

• Baseline Characteristics of Study Participants.

One hundred and sixty individuals (N=160) took part and were analyzed consisting of 50 non-complicated diabetic patients (Group I), 65 patients with complications (Group II), and 45 healthy non-diabetic controls (Group III).

Respondents' average age was 53.4 years; SD 9.8, and there was no significant difference between the two groups' average ages (p = 0.128). The sex proportions were 1.2:1 (that is, a mild degree of male dominance). The means of diabetes were 8.6 + 4.2 years of diabetes in diabetic individuals.

Table 1. Demographic and Clinical Profile of Study Participants

Interpretation: Table 1 presents the comparison of the demographic and clinical attributes of the study groups. The age and gender distribution of the participants were close and therefore indicate well-matched cohorts. But diabetic patients with complications reported a much longer time of having diabetes (9.5 ± 4.7 years) than patients without complications (6.4 ± 2.8 years) indicating that there is a strong correlation between the time of having the disease and the risk of complications. Besides, the complication group also had significantly higher both BMI and systolic blood pressure, the indicators of worse metabolic control and more cardiovascular effort. These conclusions emphasize that the long period of diabetes, being overweight of the body weight, and the high level of blood pressure are major clinical determinants of the development and degree of diabetic complications.

• Biochemical and Hematological Parameters

As expected, diabetic patients had considerably higher mean fasting blood glucose (FBG) and HbA1c than controls (p < 0.001). The complication group's mean blood creatinine and urine albumin-to-creatinine ratio (ACR) were noticeably higher, suggesting early renal system impairment.

Red Cell Distribution Width (RDW-CV) had a statistically significant progressive increase between the control, diabetic patients with complications (12.7 ± 0.8% vs 13.9 ± 1.2% vs 15.3 ± 1.6; p < 0.001).

Table 2. Comparison of Biochemical and Hematological Parameters

Interpretation: Table 2 provides the comparison of the biochemical and hematologic parameters of the controls and diabetic groups. As a marker of inadequate glycemic control, the levels of fasting blood glucose (FBG) and hemoglobin A1c were significantly elevated in individuals with diabetes, particularly in those who had complications (p < 0.001). Renal damage indicators such as albumin-to-creatinine ratio (ACR) and serum creatinine were also considerably higher in the group that had complications. Patients with diabetes who had complications exhibited a significantly wider red cell distribution width (RDW-CV) compared to controls (p < 0.001), indicating that this width is linked to the severity of their illness. The only significant difference was the slight decrease in the hemoglobin levels of diabetics, but there was no notable difference in the number of platelets. In general, these results validate the deterioration of metabolic and hematological conditions in patients with complications and diabetes.

• Association Between RDW and Diabetic Complications

RDW significantly increased in diabetic patients when further subdivided according to complication type with nephropathy and retinopathy having a higher RDW score than uncomplicated diabetes (p < 0.001).

Macrovascular compromise (i.e. peripheral vascular disease and ischemic heart disease) patients showed the greatest mean RDW (15.8 ± 1.4%).

Table 3. RDW Across Diabetic Complication Subgroups

Interpretation: Table 3 shows comparison of red cell distribution width (RDW) in various diabetic complication subgroups. Values of RDW were very significant among all diabetic populations than healthy controls (p < 0.001). The patients who had the highest RDW levels were patients with macrovascular disease (15.8 ± 1.4%), then, patients with nephropathy, and patients with retinopathy. This gradual rise in RDW between uncomplicated diabetes, microvascular and macrovascular complications indicates that high RDW indicates increased systemic inflammation, oxidative stress and endothelial dysfunction. The results suggest that RDW can be used as a convenient, cost-effective biomarker that can be used to detect diabetic patients with a greater risk of developing vascular and organ-specific issues.

Graph 1: RDW (%) Mean ± SD

Interpretation: Graph 1 depicts how the Red Cell Distribution Width (RDW %) in the various study groups varies. The control group had the least RDW value of (12.7), which represents normal variation in the size of erythrocytes. Conversely, Type 2 Diabetes Mellitus (T2DM) patients showed a progressive growth in the levels of RDW, and T2DM without complications a medium rise (13.9%). Importantly, the level of RDW in patients with nephropathy due to diabetes (15.2% and 15.0, respectively) and retinopathy (15.8% and 15.8% respectively) was the highest and was the highest among patients with several complications (15.8% and 15.8%, respectively). The trend indicates that high RDW is linked to the existence and intensity of diabetic complications, which may be due to high levels of oxidative stress, inflammation, and defects in erythropoiesis in the late stages of diabetes.

• Correlation Analysis

Correlation coefficient Pearson correlation showed a positive significant correlation in RDW and both HbA1c (r = 0.61, p < 0.001) and urine ACR (r = 0.57, p < 0.001).

"The negative correlation was observed with the estimation of the glomerular filtration rate eGFR (r = -0.48, p < 0.01)".

Table 4. Correlation of RDW With Key Clinical and Biochemical Variables

Interpretation: Table 4 shows that there is a substantial association between Red Cell Distribution Width (RDW) and critical clinical and biochemical factors in type 2 diabetes. A significant correlation between HbA1c and the variability of red blood cell size as measured by RDW (r = +0.61, p < 0.001) indicates that there is a correlation between inadequate glycemic management and a larger variability in red blood cell size. A significant connection with the albumin-to-creatinine ratio (ACR) (r = +0.57, p < 0.001) further demonstrated its association with renal microvascular injury. The inverse relationship between RDW and eGFR (r=-0.48, p=0.008) demonstrates that renal impairment improves eGFR. Also, RDW had a good correlation with diabetes duration (r = +0.42, p = 0.012), which means it might be used to anticipate how the illness would proceed and its potential effects.

Graph 2: RDW Correlation with Clinical Variables

Interpretation: In diabetic patients, the graph 2 shows the correlation of Red Cell Distribution Width (RDW) with some important clinical and biochemical variables. There is a significant correlation between RDW and HbA1c (r = +0.61, p < 0.001) and the albumin-to-creatinine ratio (ACR) (r ≈ +0.57, p < 0.001), suggesting that poorer glycaemic management and renal impairment are associated with an increase in RDW. In contrast, there is a negative correlation between RDW and estimated glomerular filtration rate (eGFR) (r ≈ -0.48, p = 0.008), suggesting that decreased kidney function is linked to high RDW. Also, the time of diabetes is positively correlated with RDW with moderate levels (r ≈ +0.42, p = 0.012), which states that RDW has the tendency to rise with the length of the disease. Overall, RDW seems to be a possible indicator that can describe glycemic and renal dysfunction in diabetes.

• Logistic Regression Analysis

Multivariate logistic regression showed that RDW of 14.5% and above was a predictor of diabetic complications (adjusted OR = 3.26, 95% CI = 1.686.32, p = 0.001) even after controlling HbA1c, period of diabetes and serum creatinine.

Table 5. Logistic Regression Showing RDW as Predictor of Diabetic Complications

Interpretation: Table 5 shows the logistic regression analysis that considers RDW as a predictor of diabetic complications. RDW 14.5% and above had been significantly related to the risk of complications, The adjusted odds ratio (OR) for patients with significant RDW was 3.26 (95% CI: 1.686.32, p = 0.001), indicating a threefold increased risk of problems. There was a strong correlation between HbA1c and the likelihood of complications, suggesting that poor glycemic management increases the likelihood of these problems (OR = 1.52, 95% CI: 1.132.04, p = 0.009). Additionally, the duration of diabetes (OR = 1.27, 95 percent CI: 1.061.52, p = 0.015) and higher serum creatinine levels (OR = 1.81, 95 percent CI: 1.122.91, p = 0.019) were also significant predictors, suggesting their involvement in the disease's development. In general, high RDW is a predictive factor of diabetic complications.

Graph 3: Clinical and Predictive Value of RDW in Diabetes

Interpretation: The clinical and predictive value of Red Cell Distribution Width (RDW) in diabetes is demonstrated in this graph 3. It shows that RDW is 14.5% and above, HbA1c is between 0 and 100, the number of years with diabetes, and serum creatinine level of 1mg/dl and above are the important parameters to predict diabetes. Out of these, the highest adjusted odds ratio

(3.26) with p-value of 0.001 was observed with RDW ≥14.5% which is a statistically significant outlier. The p-values of all the other parameters are also large (below 0.05), but the effects of RDW are the strongest. It implies that using RDW of 14.5% or above is the best biomarker of the selected ones used in clinical and risk assessment in diabetes.​

Interpretation: The clinical and predictive value of Red Cell Distribution Width (RDW) in diabetes is demonstrated in this graph 3. It shows that RDW is 14.5% and above, HbA1c is between 0 and 100, the number of years with diabetes, and serum creatinine level of 1mg/dl and above are the important parameters to predict diabetes. Out of these, the highest adjusted odds ratio

(3.26) with p-value of 0.001 was observed with RDW ≥14.5% which is a statistically significant outlier. The p-values of all the other parameters are also large (below 0.05), but the effects of RDW are the strongest. It implies that using RDW of 14.5% or above is the best biomarker of the selected ones used in clinical and risk assessment in diabetes.​

The current research paper has shown that Red Cell Distribution Width (RDW) is considerably higher among patients with type 2 diabetes mellitus (T2DM) than it is in healthy individuals, and with the development and severity of diabetic complications. Patients with macrovascular disease had the highest mean RDW, then patients with nephropathy and retinopathy, indicating that anisocytosis may be the result of the underlying systemic inflammation, oxidative stress, and endothelial dysfunction of diabetic patients. These results are consistent with other studies conducted by Malandrino et al. (2012) and Li et al. (2022), who have defined RDW as a new Hematological factor to predict both microvascular and macrovascular problems of diabetes.

A positive connection of 0.61 was observed between RDW and HbA1c, a negative correlation of -0.48 was identified with eGFR, and a positive correlation of 0.57 with albumin to creatinine ratio and 0.42 with duration of diabetes, respectively. These associations highlight that inadequate glycaemic regulation, extended disease, and kidney failure are some of the risk factors that result in anisocytosis. In their investigation Zhang et al. (2020) and Lin et al. (2022) also concluded that RDW is negatively associated with eGFR, but positively connected with indicators of renal injury, which proves its involvement in the pathogenesis of diabetic nephropathy.

Mechanistically, persistent hyperglycaemia triggers oxidative stress, which causes the destruction of the erythrocyte membranes, decreases the length of the RBC livespan, and enhances the cell size heterogeneity. TNF-a and IL-6 inflammatory cytokines also impair erythropoiesis, which results in high RDW. This observation was supported in studies by Semba et al. (2010) and Hu et al. (2019) who attributed RDW to systemic inflammation and erythropoietic dysregulation. Also, higher RDW is a sign of endothelial dysfunction, which is a risk factor to atherosclerotic vascular disease, a finding that is in line with that reported by Tsuboi et al. (2013), who reported that higher RDW was good enough to predict long-term mortality in diabetics following coronary stent implantation.

The study found out that, despite adjusting the serum creatinine and diabetes life span, and HbA1c, a RDW of 14.5% owing to no interaction effect among any of the three variables, was a significant predictor of diabetic sequelae. This is consistent with previous meta-analyses that observed higher RDW to be an independent predictor of vascular morbidity and mortality in diabetic individuals. The good predictive capability of RDW indicates that it is a useful inexpensive biomarker to use in the detection of high-risk patients. The absence of any significant anaemia suggests that there is systemic metabolic stress, as well as hematologic deficit, the cause of the increase in RDW.

In addition, the current results suggest that RDW is associated with microvascular (nephropathy, retinopathy) and macrovascular (atherosclerosis peripheral artery disease) damages. The two associations suggest that there is some common pathophysiology between endothelial damage and oxidative imbalance. These data are consistent with Pan et al. (2019) and Chen et al. (2023) who discovered that RDW-to-albumin ratios and RDW-to-hemoglobin indices can be more effective to diagnose diabetic

 kidney disease.

RDW in clinical terms is a cost-effective, non-invasive biomarker that can be incorporated in current complete blood count testing of diabetic patients. Its application would be able to enable the timely diagnosis of individuals at increased risk of complications, and timely interventions like enhanced glycaemic regulation, antioxidant supplementation, and cardiovascular risk management. RDW on regular diabetic screening as suggested by Gao et al. (2021) has the potential to improve patient stratification and decrease the healthcare burden on diabetic complications.

Nonetheless, several confounding factors potentially affecting RDW are known, and they include chronic inflammation not related to diabetes, abnormal iron metabolic, or liver disease. As such, RDW is not to be understood separately but taken as an additional indicator along with both biochemical and clinical evaluations. Regardless of these shortcomings, the study is a good piece of evidence that RDW is an independent predictor of vascular damage and risk of complication in diabetes.

Generally, the results of this paper support the emerging opinion that RDW is a surrogate endpoint of vascular dysfunction of the whole system in diabetes. Its close correlation with the prevention of glycaemic control and end-organ damage justify its role in the future diagnostic and prognostic model of diabetic care.

This current study shows that the Red Cell Distribution Width (RDW) is an important, inexpensive, and readily available biomarker that can be utilized to evaluate the threat of diabetic microvascular and macrovascular complications. Higher RDW scores were closely linked to inadequate glycaemic control, kidney disorder, and increased disease length which indicated oxidative stress, inflammation and endothelial damage. The RDW ≥14.5% was found to be a separate predictor of diabetic complications by use of logistic regression. Such results indicate the potential of RDW as a viable screening method to detect and stratify risk in diabetes mellitus type 2 at its early stages. The introduction of RDW into the routine parts of diabetic care may contribute to the timely intervention, better management, and decreased long-term vascular morbidity related to diabetes.

1. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37(Suppl 1): S81–S90.
2. Fowler MJ. Microvascular and macrovascular complications of diabetes. Clin Diabetes. 2008;26(2):77–82.
3. Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev. 2013;93(1):137–188.
4. Stratton IM, Adler AI, Neil HAW, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes. BMJ. 2000;321(7258):405–12.
5. Malandrino N, Wu WC, Taveira TH, Whitlatch HB, Smith RJ. Association between red blood cell distribution width and macrovascular and microvascular complications in diabetes. Diabetologia. 2012;55(1):226–35.
6. Li N, Zhou H, Tang Q. Red blood cell distribution width: A novel predictive marker for cardiovascular and kidney diseases. Front Med. 2022;9:850649.
7. International Diabetes Federation. IDF Diabetes Atlas. 10th ed. Brussels: IDF; 2021.
8. Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: Challenges, progress, and possibilities. Clin J Am Soc Nephrol. 2017;12(12):2032–45.
9. Yau JWY, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556–64.
10. Sarwar N, Gao P, Seshasai SRK, Gobin R, Kaptoge S, Di Angelantonio E, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: A collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–22.
11. American Diabetes Association. Standards of medical care in diabetes—2022. Diabetes Care. 2022;45(Suppl 1):S1–262.
12. Semba RD, Patel KV, Ferrucci L, Sun K, Roy CN, Guralnik JM, et al. Serum antioxidants and inflammation predict red cell distribution width in older women. J Gerontol a Biol Sci Med Sci. 2010;65(3):376–81.
13. Li Y, Fan L, Li J, Wei P, Zhang Y, Wang C, et al. Association between red blood cell distribution width and renal function in patients with type 2 diabetes mellitus. J Diabetes Complications. 2017;31(9):1381–6.
14. Lippi G, Salvagno GL, Guidi GC. Red blood cell distribution width is significantly associated with aging and gender. Clin Chem Lab Med. 2014;52(9):e197–9.
15. Ma Y, Zhang H, Zhang B, Wang J, Li J, Yang H, et al. Association between red cell distribution width and diabetic retinopathy in patients with type 2 diabetes mellitus. J Diabetes Complications. 2021;35(10):107971.
16. Chen J, Li X, Zhong L, Zhou H, Zhang R, Xu Y, et al. red blood cell distribution width to albumin ratio as a predictor of diabetic kidney disease. Front Endocrinol (Lausanne). 2023;14:1118723.
17. Hu ZD, Lippi G, Montagnana M. Diagnostic and prognostic value of red blood cell distribution width in sepsis and systemic inflammatory response syndrome. Clin Chim Acta. 2019;493:111–8.
18. Zhang J, Zhang R, Wang Y, He J, Zhang L, Wang H, et al. Elevated red cell distribution width as an independent risk marker of diabetic vascular complications. Medicine (Baltimore). 2020;99(49):e23410.
19. Lin C, Zhang Y, Chen W, Huang S, Zhou Y, Wang X, et al. Association of red blood cell distribution width with chronic kidney disease in patients with type 2 diabetes. BMC Endocr Disord. 2022;22:181.
20. Gao X, Yang H, Ma L, Li Y, Zhang B, Wang J, et al. The predictive value of red blood cell distribution width for all-cause mortality in diabetic patients: A systematic review and meta-analysis. PLoS One. 2021;16(2):e0247166.
21. Salvagno GL, Picanza A, Lippi G. Red blood cell distribution width: Historical background, clinical significance, and future perspectives. Clin Chem Lab Med. 2017;55(1):30–5.
22. Pan X, Yang X, Li S, Liu Y, Zhou J, Zhang Z, et al. Association between red cell distribution width and microalbuminuria in type 2 diabetes. Int Urol Nephrol. 2019;51(5):871–7.
23. Tsuboi S, Miyauchi K, Kasai T, Ogita M, Dohi T, Miyazaki T, et al. Impact of red blood cell distribution width on long-term mortality in diabetic patients after coronary stent implantation. Circ J. 2013;77(2):456–61.
24. Li N, Wang Y, Zhou H, Tang Q, Li H, Chen J. Elevated red blood cell distribution width predicts microvascular complications and mortality in type 2 diabetes: A meta-analysis. Diabetes Metab Syndr. 2024;18(1):102885.
25. Rush T, McGeary M, Sicignano N, Buryk MA. A plateau in new onset type 1 diabetes: Incidence of pediatric diabetes in the United States Military Health System. Pediatr Diabetes. 2018 Aug;19(5):917- 922. [PubMed]
26. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018 Feb;14(2):88-98. [PubMed]
27. de Boer IH, Bangalore S, Benetos A, Davis AM, Michos ED, Muntner P, Rossing P, Zoungas S, Bakris G. Diabetes and Hypertension: A Position Statement by the American Diabetes Association. Diabetes Care. 2017 Sep;40(9):1273-1284. [PubMed]
28. Unnikrishnan AG, Kalra S, Purandare V, Vasnawala H. Genital Infections with Sodium Glucose Cotransporter-2 Inhibitors: Occurrence and Management in Patients with Type 2 Diabetes Mellitus. Indian J Endocrinol Metab. 2018 Nov-Dec;22(6):837-842. [PMC free article] [PubMed]
29. Heron M. Deaths: Leading Causes for 2015. Natl Vital Stat Rep. 2017 Nov;66(5):1-76. [PubMed]
30. Yamazaki D, Hitomi H, Nishiyama A. Hypertension with diabetes mellitus complications. Hypertens 2018    Mar;41(3):147-156. [PubMed]
31. Salvagno GL, Sanchis-Gomar F, Picanza A, Lippi G. Red blood cell distribution width: A simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci. 2015;52(2):86–105.
32. Malandrino N, Wu WC, Taveira TH, Whitlatch HB, Smith RJ. Association between red blood cell distribution width and microvascular and macrovascular complications in diabetes. 2012;55(1):226–35.
33. Pan J, Xu L, Wei W, Zhuang Z, Zhou L. Relationship between red cell distribution width and microalbuminuria in type 2 diabetic patients. Ren Fail. 2019;41(1):713–9.
34. Ma X, Li C, Chen S, Liang Y, Li L, Chen Y. Association between red blood cell distribution width and diabetic retinopathy in type 2 diabetes. BMC Ophthalmol. 2021;21(1):271.
35. Xiong XF, Yang Y, Chen X, Zhu X, Hu C, Han Y, et al. red cell distribution width as a significant indicator of medication and prognosis in type 2 diabetic patients. Sci Rep. 2017;7(1):2709.
36. Aml Mohamed Nada. Red cell distribution width in type 2 diabetic patients. Diabetes Metab Syndr Obes. 2015;8:525–33.
37. Xanthopoulos A, Giamouzis G, Melidonis A, et al. red blood cell distribution width as a prognostic marker in patients with heart failure and diabetes mellitus. Cardiovasc Diabetol. 2017;16:81.
38. Nian S, Lo ACY, Mi Y, Ren K, Yang D. Neurovascular unit in diabetic retinopathy: pathophysiological roles and potential therapeutical targets. Eye Vis (Lond). 2021;8(1):15.