Predictive Role of Hematological Biomarkers in Chronic Kidney Disease Progression

ABSTRACT

Background

Chronic kidney disease is a progressive disorder of the body with high morbidity. Hematological biomarkers can predict CKD progression.

Objective

This study examined the predictive role of hematological parameters among adult CKD patients.

Methods

The records of 120 adult patients with CKD were retrieved. CKD staging was according to KDIGO guidelines. Hematological parameters were hemoglobin, WBC, percentages of neutrophils and lymphocytes, NLR, platelet count, MCV, and RDW. Data were analyzed to assess associations between hematological markers and disease stage.

Results

Mean age was 56.4 ± 13.2 years, with 56.7% being male. Prevalence was 65.0% for hypertension and 38.3% for diabetes mellitus. There was a significant decrease in hemoglobin with CKD stage (13.4 ± 1.1 g/dL in Stage 1 to 8.5 ± 1.7 g/dL in Stage 5, p < 0.001), while NLR and RDW increased progressively with CKD stage (NLR: 1.55 ± 0.48 to 4.12 ± 1.02; RDW: 13.1% ± 0.8% to 16.0% ± 1.6%, both p < 0.001). Anemia and raised NLR were more frequent in the advanced stages of CKD. Logistic regression analysis identified hemoglobin (OR = 0.69, 95% CI: 0.58–0.82, p < 0.001), RDW (OR = 1.78, 95% CI: 1.33–2.39, p = 0.002), and NLR (OR = 1.91, 95% CI: 1.35–2.72, p = 0.001) as independent predictors of advanced CKD. These simple and inexpensive biomarkers are particularly valuable in resource‐limited settings.

Conclusion

Hematological biomarkers, especially hemoglobin, NLR, and RDW, were effectively used to predict the progression of CKD.

This retrospective study assessed the predictive value of hematological biomarkers in the progression and complications of chronic kidney disease (CKD) among 120 adult patients. Key findings revealed that declining hemoglobin and increasing red cell distribution width (RDW) and neutrophil‐to‐lymphocyte ratio (NLR) were significantly associated with advanced CKD stages, underscoring their potential role as accessible, cost‐effective prognostic tools in resource‐limited settings.

1. Introduction

Chronic kidney disease (CKD) has been advancing as a public health problem in sub‐Saharan Africa, including Kenya, with a possible range of 10%–15% of the Kenyan population affected by this condition [1]. Currently, there is an ongoing rise in the rates of hypertension, diabetes, and infectious diseases that contribute to the increasing burden of CKD, often diagnosed late with poor prognosis [2]. Identifying patients who progress with CKD rapidly at an early stage would allow for timely intervention and hence reduce the high morbidity and mortality associated with this disease [3].

Beyond erythropoiesis, chronic inflammation also results in abnormal hematological parameters in CKD patients [4]. Hemoglobin concentration, neutrophil‐lymphocyte ratio (NLR), and red cell distribution width (RDW) have recently been discussed as potential prognostic indices that reveal CKD progression and associated complications [5]. These are inexpensive, accessible in laboratories, and of particular value in resource‐constrained environments. Beyond CKD, hematological biomarkers have shown prognostic value in other chronic conditions. For example, NLR and RDW have been linked to cardiovascular disease outcomes and complications of type 2 diabetes mellitus [6]. Such evidence underscores the broader clinical relevance of these parameters as markers of systemic inflammation and disease progression. However, data remain limited in sub‐Saharan Africa, where their cost‐effectiveness and accessibility could provide a unique advantage for clinical decision‐making [7].

However, little is known about the prognostic role of hematological indicators in patients with CKD within Kenyan health facilities [8]. The aim of this study was to investigate the prognostic role of hematological parameters in CKD progression among adult patients, thereby contributing locally relevant evidence to guide clinical practice and improve patient outcomes.

2. Methods

2.1. Study Design

This was a retrospective study among chronic kidney disease patients.

2.2. Study Population

A total of 120 patient records deemed eligible were analyzed. Included were adult patients (age 18 years and above) with confirmed cases of CKD by eGFR and clinical records. This CKD staging was performed using the KDIGO 2012 guidelines [9]. Patients with acute kidney injury, hematologic malignancies, or active infections at the time of hematological testing were excluded. Such a sample size was defined by the number of complete and valid records available within the study period without imputation.

2.3. Data Collection

Data were gathered on demographics (age, sex), clinical variables (CKD stage, presence of hypertension or diabetes), and hematological parameters. The hematological biomarkers of interest were obtained from the CBC results: Hemoglobin, WBC count, percentages of Neutrophils and Lymphocytes, Neutrophil‐to‐lymphocyte ratio, Platelets, MCV, and RDW. The categorization of CKD‐related complications was as follows: Anemia: Hemoglobin < 10 g/dL, Leukocytosis: WBC > 10 × 10⁹/L, Thrombocytopenia: Platelets < 150 × 10⁹/L, High NLR: NLR > 3.5, High RDW: RDW > 14.5%.

2.4. Statistical Analysis

Data analysis was performed using SPSS version 26 (IBM Corp., Armonk, NY, USA). Cutoffs for high NLR (> 3.5) and high RDW (> 14.5%) were based on prior studies demonstrating their prognostic significance in renal and cardiovascular disease populations [10]. The Shapiro–Wilk test was used to test continuous variables for normality. Descriptive statistics for independently distributed variables were expressed as means and standard deviations (SDs), and frequencies and percentages for categorical variables. Comparison between CKD stages (Stages 1–5) was by one‐way ANOVA for normally distributed continuous variables or Kruskal–Wallis test for non‐normal variables while categories were compared using a chi‐squared or in cases where the conditions were not satisfied, a Fisher's exact test. A binary logistic regression model was used to identify independent hematological predictors of advanced CKD (defined as Stage 4 or 5), and its covariates were age, hypertension, diabetes, hemoglobin, RDW, NLR, and WBC count. Adjusted odds ratios (ORs), with 95% confidence intervals (CIs) were reported. Unless otherwise stated, all tests were performed at the significance level of p < 0.05.

2.5. Ethical Considerations

All ethical procedures were in line with the Declaration of Helsinki [11] and WHO guidelines on research involving human subjects. Data confidentiality was achieved through the de‐identification and anonymization of data at the time of its extraction.

3. Results

3.1. Demographic and Clinical Characteristics

The study involving 120 patients of adult age, all having CKD, formed the scope of this study. The mean age is 56.4 (±13.2) years; there were 68 males (56.7%) and 52 females (43.3%). Hypertension was found in 78 patients (65.0%), and 46 (38.3%) had diabetes mellitus. Distribution according to CKD stage was Stage 1 (n = 8, 6.7%), Stage 2 (n = 12, 10.0%), Stage 3 (n = 40, 33.3%), Stage 4 (n = 28, 23.3%), and finally, Stage 5 (n = 32, 26.7%). The information is further supplemented in Table 1.

TABLE 1.

Demographic and clinical characteristics by CKD stage (N = 120).

Variable	Stage 1 (n = 8)	Stage 2 (n = 12)	Stage 3 (n = 40)	Stage 4 (n = 28)	Stage 5 (n = 32)	p
Mean age (±SD)	49.3 ± 12.1	52.4 ± 10.9	54.8 ± 13.4	59.7 ± 14.0	61.3 ± 12.8	0.021
Male sex (%)	5 (62.5%)	7 (58.3%)	24 (60.0%)	15 (53.6%)	17 (53.1%)	0.928
Hypertension (%)	3 (37.5%)	5 (41.7%)	27 (67.5%)	21 (75.0%)	22 (68.8%)	0.048
Diabetes (%)	2 (25.0%)	4 (33.3%)	13 (32.5%)	13 (46.4%)	14 (43.8%)	0.494

3.2. Hematological Biomarker Levels by CKD Stage

One‐way ANOVA was used for normally distributed variables. A progressive deterioration was noticed in hematological parameters through evolving tests into the CKD stages; such as significantly dropping hemoglobin levels from Stage 1 (13.4 ± 1.1 g/dL) to Stage 5 (8.5 ± 1.7 g/dL), p < 0.001 as shown in Table 2. NLR and RDW also showed significant increases in stages with advanced development. See in Figure 1 and Table 2.

TABLE 2.

Hematological biomarkers across CKD stages.

Biomarker	S1	S2	S3	S4	S5	p
Hemoglobin (g/dL)	13.4 ± 1.1	12.8 ± 1.3	11.7 ± 1.5	10.1 ± 1.6	8.5 ± 1.7	< 0.001
WBC (×109/L)	5.8 ± 1.0	6.1 ± 1.1	6.5 ± 1.2	7.2 ± 1.4	8.1 ± 1.6	0.004
Neutrophils (%)	54.2 ± 6.3	56.7 ± 6.8	58.5 ± 7.0	61.8 ± 6.4	65.1 ± 6.9	0.012
Lymphocytes (%)	34.9 ± 5.6	33.2 ± 6.1	30.1 ± 6.7	28.4 ± 5.9	26.0 ± 5.5	0.017
NLR	1.55 ± 0.48	1.82 ± 0.52	2.42 ± 0.76	3.11 ± 0.89	4.12 ± 1.02	< 0.001
Platelets (×109/L)	246 ± 50	240 ± 48	232 ± 47	218 ± 54	209 ± 58	0.110
RDW (%)	13.1 ± 0.8	13.5 ± 1.0	14.3 ± 1.2	15.1 ± 1.4	16.0 ± 1.6	< 0.001
MCV (fL)	85.2 ± 3.4	86.1 ± 3.7	87.0 ± 4.1	88.5 ± 4.5	89.2 ± 5.0	0.065

FIGURE 1.

Hematological biomarkers across CKD stages.

3.3. Hematological Abnormalities by CKD Stage

To further characterize the distribution of hematologic abnormalities such as anemia (Hb < 10 g/dL), leukocytosis, thrombocytopenia, and high inflammatory indices, Table 3 depicts their prevalence across the different stages of CKD. Proportions were compared between groups using the chi‐squared test, and Fisher's exact test was applied when the numbers in the expected cells were too low. For anemia, that is Hb < 10 g/dL, 71.9% of patients at Stage 5 versus 0% at Stage 1 were affected; and in patients with Stage 5, 62.5% presented with elevated NLR (> 3.5), a marker for systemic inflammation.

TABLE 3.

Prevalence of hematological abnormalities across CKD stages.

Abnormality	S1 (%)	S2 (%)	S3 (%)	S4 (%)	S5 (%)	p
Anemia (Hb < 10)	0	0	10 (25.0%)	18 (64.3%)	23 (71.9%)	< 0.001
Leukocytosis (> 10 WBC)	0	1 (8.3%)	2 (5.0%)	5 (17.9%)	9 (28.1%)	0.006
High NLR (> 3.5)	0	0	5 (12.5%)	10 (35.7%)	20 (62.5%)	< 0.001
Thrombocytopenia	0	0	1 (2.5%)	2 (7.1%)	5 (15.6%)	0.041
High RDW (> 14.5%)	0	1 (8.3%)	11 (27.5%)	18 (64.3%)	24 (75.0%)	< 0.001

3.4. Predictors of CKD Progression

Binary logistic regression was conducted to identify independent hematological predictors of advanced CKD (Stage 4–5 vs. Stage 1–3). Hemoglobin (OR = 0.69, 95% CI: 0.58–0.82, p < 0.001), RDW (OR = 1.78, 95% CI: 1.33–2.39, p = 0.002), and NLR (OR = 1.91, 95% CI: 1.35–2.72, p = 0.001) were significant. See in Figure 2 and Table 4.

FIGURE 2.

Forest plot of predictors of advanced CKD.

TABLE 4.

Logistic regression analysis for predictors of advanced CKD.

Predictor	OR (95% CI)	p
Hemoglobin (g/dL)	0.69 (0.58–0.82)	< 0.001
RDW (%)	1.78 (1.33–2.39)	0.002
NLR	1.91 (1.35–2.72)	0.001
WBC (×109/L)	1.18 (0.96–1.44)	0.114
Hypertension	1.43 (0.78–2.64)	0.239
Diabetes	1.26 (0.67–2.38)	0.465
Age (years)	1.02 (0.99–1.05)	0

Figure 2 displays adjusted odds ratios (OR) with 95% confidence intervals (CI) for the association of hemoglobin, neutrophil‐to‐lymphocyte ratio (NLR), and red cell distribution width (RDW) with chronic kidney disease (CKD) stages. The model was adjusted for age, sex, hypertension, and diabetes mellitus. The vertical line at OR = 1 represents the null value (no association). Variables included in the model were hemoglobin, RDW, NLR, WBC count, hypertension, diabetes, and age.

4. Discussion

This study was conducted to assess the predictive role of hematological biomarkers in CKD progression among adult patients. The study results showed significant alterations in hematological parameters across the advancing CKD stages, specifically decreasing hemoglobin and increasing neutrophil‐to‐lymphocyte ratio (NLR) and red cell distribution width (RDW). These markers independently predicted advanced CKD (Stages 4–5), aligning with prior research that implicates anemia and systemic inflammation in CKD progression.

The progressive decline in hemoglobin concentration seen across CKD stages is consistent with the accepted concept that anemia becomes more common the worse renal function becomes due to diminished erythropoietin and disturbances in iron metabolism [12]. However, anemia prevalence at late stages (71.9% in Stage 5) coincides with reports on similar cohorts from sub‐Saharan Africa and the rest of the world [3, 13]. In contrast, our atypically low anemia rates at early stages diverge from some studies where anemia onset is reported earlier and might allude to differences in patient demographics, nutritional status, or healthcare access. The significant elevation in NLR with CKD progression supports a potential role of the NLR as a systemic inflammatory marker, which further causes endothelial dysfunction and cardiovascular complications in CKD. Our observation that higher NLR predicts advanced CKD aligns with meta‐analyses affirming NLR‐prognostic value [14, 15], though few studies find NLR to be highly variable, possibly a reflection of differences in inflammatory burden and patterns of comorbid conditions.

An increase in NLR and RDW in slow progression of CKD could be explained mechanistically by chronic inflammation and oxidative stress. Inflammatory cytokines maintain neutrophilia and suppress lymphocytes, thereby raising NLR; meanwhile, oxidative stress and defective erythropoiesis increase heterogeneity in red blood cell size, reflected in increased RDW. Similar findings were encountered in other renal and vascular situations, with Çiçek et al. [15] showing that inflammation‐related biomarkers such as neopterin predict kidney injury after cardiac surgery. While more specific markers do exist, NLR and RDW have the distinction of being readily accessible and cheap, thus preferred in resource‐poor settings [16]. RDW, meanwhile, increased markedly with CKD progression, and our findings again bear witness to previous evidence associating increased RDW with inflammation, oxidative stress, and poor outcomes in CKD patients [17, 18]. Contrary to those other studies, ideas emerge as RDW seems to hold independent prognostic implications in our population, thereby signifying its potential use as an easy and inexpensive biomarker in resource‐limited settings. Intriguingly, while leukocytes increased with increasing disease severity, neither platelets nor MCV showed a significant change, in some contrast to other reports of thrombocytopenia in advanced CKD [2, 19]. This discrepancy may be due to differences in sample size, population characteristics, or underlying etiologies. Our findings attest to the value of routinely assessed hematological parameters to monitor CKD progression and risk stratification. This study adds to the limited data from Kenya and sub‐Saharan Africa, focusing on the use of these easily accessible biomarkers in day‐to‐day care.

An integration of hematological markers being incorporated into the surveillance machinery of CKD has enormous repercussions for public health, particularly in low‐resource settings. In Kenya and areas of a similar nature, where advanced renal biomarkers are a restricted access service, NLR and RDW may provide a simple, scalable methodology for early detection and monitoring of CKD progression. This corroborates findings in vascular disease studies, in which NLR predicted the risk of amputation post‐acute limb ischemia [20, 21], basic blood tests mirrored the peripheral arterial lesions [22, 23]. Our outcomes strengthen the claim that such simple hematologic markers may offer fully meaningful prognostic information in areas where the diagnostic might of more sophisticated tests is just unavailable.

5. Limitations

There are several limitations deserving consideration. First, with a retrospective design, there is a limitation in causal inference and it becomes a little more prone to missing or incomplete information. Secondly, 120 patients might be enough for a pilot study, but the relatively few cases with Stage 1 and Stage 2 limited the precision in comparing all the CKD stages. Third, other confounders such as medication use, nutrition, or subclinical inflammatory states (occult infections or autoimmune disorders) might have altered hematological indices independent of CKD progression but were not recorded. Selection bias could have affected results, given the location. Larger prospective multicenter studies are necessary to validate these findings.

6. Conclusion

The study found that hematological biomarkers, such as hemoglobin, neutrophils to lymphocytes ratio (NLR), and red cell distribution width, are statistically significantly associated with the progression of CKD in adult patients. These parameters are readily available and affordable and could be used for early identification of CKD patients at risk for advanced stage to institute early intervention to improve outcomes. Routine hematological evaluation should, therefore, be integrated into CKD management protocols, especially in resource‐constrained settings.

Author Contributions

Collince Odiwuor Ogolla conceived the study, collected and analyzed data, and drafted the manuscript. Lucy W. Karani and Stanslaus Musyoki contributed to study design, data interpretation, and critical revision of the manuscript. Phidelis Maruti contributed to data analysis, interpretation, and manuscript revision. All authors meet the ICMJE criteria for authorship: (1) substantial contributions to the conception, design, acquisition, analysis, or interpretation of data; and (2) drafting or revising the manuscript critically for important intellectual content. All authors approved the final version for submission.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study is all shared on this article.