Role of Inflammatory and Hematologic Biomarkers in Predicting Chronic Kidney Disease Progression

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Karani, Stanslaus Musyoki, Phidelis Maruti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6710388/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic kidney disease (CKD) is a progressive condition that as such entails huge morbidity and mortality. Identification of dependable biomarkers that could predict the progression of CKD can provide an opportunity for risk stratification and early intervention. This study evaluates the role of inflammatory and hematologic biomarkers in the 1-year prediction of CKD progression. Methods The study was prospective and observational, with 120 adults recruited with CKD stages 1–4, from April 2024 to April 2025. Baseline data including the demographic characteristics, comorbidities, and laboratory parameters were collected. The biomarkers studied included CRP, NLR, PLR, hemoglobin, WBC count, and serum ferritin. CKD progression was defined as a persistent decline of ≥ 25% in eGFR or movement to a higher KDIGO stage. Statistical application included t-tests, coefficients of correlation, multivariate logistic regression, and ROC curve analysis. Results Within a 12-month follow-up period, CKD progression was documented in 42 patients (35.0%) and was most frequent among patients in CKD stage 3 at baseline. Progressors recorded significantly higher values of CRP, NLR, PLR, and ferritin and lower values of hemoglobin ((p < 0.05). On multivariate analysis, CRP, NLR, and hemoglobin emerged as independent predictors of progression. ROC analysis was conducted showing good prediction ability for CRP (AUC 0.78), hemoglobin (AUC 0.75), and NLR (AUC 0.72). Conclusion Elevated inflammatory and hematologic biomarkers, particularly CRP, NLR, and low hemoglobin, are significantly associated with the progression of CKD. These biomarkers could be employed as cheap and quick differentiating tools for high-risk CKD patients while fostering clinical monitoring documentation. Chronic kidney disease CRP NLR hemoglobin biomarkers progression inflammation Figures Figure 1 Figure 2 Introduction Chronic kidney disease (CKD) is an existing and fast-rising worldwide health concern, affecting a prodigious 10% of the global populace and a large share of mortality and morbidity (1). The progression from CKD to an end-stage renal condition brings in expensive interventions, such as dialysis or kidney transplantation, which weigh heavy loads on the global healthcare system (2). Early detection of those who have a high chance of progressing through CKD is very valuable in applying timely and effective preventative measures that could retard the loss of renal function and improve the final outcome for the patient. The course of CKD is determined by many factors, the most important being the comorbidities frequently accompanying CKD, such as hypertension, diabetes, and cardiovascular diseases (3). Alongside, mounting evidence supports that inflammation is an important player in the pathogenesis of CKD. Inflammatory biomarkers such as CRP are usually elevated in CKD patients and are found to be associated with adverse outcomes, faster decline of renal functions, and progression to ESRD (4). CRP, an inflammatory biomarker, is postulated to cause endothelial dysfunction, glomerular injury, and fibrosis, collectively leading to accelerated kidney damage (5). In addition to CRP, hematologic biomarkers such as neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have emerged as potential indicators of systemic inflammation and immune dysregulation in CKD (6). These ratios describe an imbalance between the inflammatory and anti-inflammatory parts of the immune system and may provide information about the extent of kidney injury. The available literature lacks in-depth examination of NLR and PLR capabilities for predicting CKD advancement. Anemia becomes a frequent secondary condition in CKD cases mainly during the advanced stages while research proves its association with poor outcomes including kidney disease acceleration (7). Low hemoglobin levels frequently occur in patients with CKD because of erythropoietin deficiency and chronic inflammation and these levels lead to higher kidney function decline risks (8). A rising number of studies show interest in using these biomarkers to forecast CKD progression yet research on their actual predictive capabilities remains insufficient. Research aimed to evaluate CRP and NLR and PLR and hemoglobin as predictive factors for CKD progression during a one-year tracking period. Our research expected that heightened biomarker levels would link to accelerated CKD progression especially in patients with mid-stage CKD. The research establishes new knowledge regarding biomarkers that could lead to their clinical application for early risk assessment and personalized interventions in CKD patients. Methods Study Design The study was a prospective observational study conducted for twelve months from April 2024-April 2025 in Kisii Teaching and Referral Hospital. This study looked at the potential predictive value of selected inflammatory and hematologic biomarkers in the risk of CKD progression. Study Population One hundred twenty adult patients with CKD stages 1–4 diagnosed through KDIGO guidelines were consecutively sampled from the nephrology outpatient clinics. Inclusion criteria; Age ≥ 18 years, Diagnosed with CKD stage 1, 2, 3a, 3b, or 4, Stable clinical condition (no acute illness in past 4 weeks), Available for follow-up for 12 months, Willing to participate and undergo blood investigations. Exclusion criteria; AKI or rapid eGFR decline within 3 months before enrollment, Active infection, malignancy, autoimmune disease flare, Use of immunosuppressive therapy, Pregnancy or lactation, Incomplete biomarker or follow-up data. Sample Collection and Laboratory Analysis Venous blood was drawn from all participants at baseline applying sterile techniques into standard vacutainer tubes. The samples were drawn after fasting for at least 8 h, processed within 2 h of blood collection, and analyzed in the central clinical laboratory. Laboratory parameters include: C-reactive protein (CRP): Measured using a high sensitivity immunoturbidimetric assay; Complete blood count (CBC): Performed using an automated hematology analyzer (Sysmex XN-500i), from which neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were calculated, Hemoglobin, WBC, and platelet count: Derived from CBC, Serum ferritin: Measured by chemiluminescent immunoassay, Serum creatinine: Measured using an enzymatic method; eGFR was calculated using the CKD-EPI equation. Repeat measurements were taken at 12 months to determine progression of CKD based on changes in eGFR value and CKD stage. Demographic, clinical, and laboratory data were recorded at the time of enrolment. This included: Age, sex, comorbidities (hypertension, diabetes, cardiovascular disease), Serum creatinine, and eGFR (calculated using the CKD-EPI formula); Complete blood count and inflammatory markers. Participants underwent laboratory testing at enrollment and at the end of the follow-up period of 12 months as per standard care procedure. CKD staging was assigned based on baseline and end-of-study eGFR values. The following inflammatory and hematologic biomarkers, amongst others, were measured at baseline: C-reactive protein (CRP) (mg/L), Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) (calculated from CBC), Hemoglobin (g/dL), White blood cell (WBC) count (×10⁹/L), Serum ferritin (ng/mL). Laboratory work was done at one centrally accredited laboratory by automated analysers. Biomarker values were compared between those who did and those who did not progress. Statistical Analysis Data analyses were performed by means of SPSS v26.0 software (IBM Corp., Armonk, NY). Statistical significance was set at p < 0.05. Descriptive statistics were used to summarize baseline characteristics and biomarker distributions. Independent t-tests or Mann–Whitney U tests compared continuous variables between progressors and non-progressors. Pearson and Spearman correlation coefficients assessed relationships between biomarkers and change in eGFR (ΔeGFR). A multivariate logistic regression model was constructed to identify independent predictors of CKD progression after adjusting for age, sex, baseline eGFR, hypertension, and diabetes. ROC curve analysis was conducted to examine the diagnostic performance of main biomarkers (CRP, NLR, hemoglobin) in predicting CKD progression, with the AUC, sensitivity, specificity, and optimal cut-off points being reported. For any missing data, no imputation methods were applied, and complete-case analysis was used. Ethical Consideration Ethical approval waiver was granted by the IRB of Kisii Teaching and Reporting Hospital. The study observed ethics according to Helsinki declaration and all participants signed voluntary written informed consent form before enrollment. Confidentiality regarding participants was maintained throughout the study; all the data were anonymized and stored securely. Additionally, the participants were informed that they would withdraw from the study without penalty at any time. This current study does not include identifying images or other personal or clinical details of participants that compromise anonymity hence consent for publication is not applicable. This study was not an experimental study hence experimental protocol approvals were not required. Results Demographic Characteristics The study included a total of 120 patients with a clinical diagnosis of Chronic Kidney Disease (CKD). They had an average age of 58.4 ± 13.6 years; with 53.3% (n = 64) males and 46.7% (n = 56) females. As per the mean values measured at baseline, eGFR was 49.2 ± 18.7 mL/min/1.73m², while serum levels of creatinine were 1.9 ± 0.8 mg/dL. Baseline comorbidities included: Hypertension: 82 patients (68.3%), Diabetes mellitus: 58 patients (48.3%), Cardiovascular disease: 29 patients (24.2%). The study lasted for one year, and progression of CKD was defined as: Sustained 25% decline in eGFR or Progression to a higher CKD stage (KDIGO guidelines-based). Biomarker Profile and Summary Statistics The inflammatory and hematologic biomarkers were assessed as shown in table 1: Table 1: Biomarker Profile and Summary Statistics Biomarker Mean ± SD Reference Range C-reactive protein (CRP) 6.2 ± 3.1 mg/L < 3 mg/L Neutrophil-to-lymphocyte ratio (NLR) 3.8 ± 1.9 1.0–3.0 Platelet-to-lymphocyte ratio (PLR) 160.4 ± 48.6 100–200 Hemoglobin 11.2 ± 1.5 g/dL 13–17 g/dL (M), 12–15 g/dL (F) White blood cell (WBC) count 7.3 ± 1.8 ×10⁹/L 4.0–11.0 ×10⁹/L Serum ferritin 198.6 ± 88.4 ng/mL 30–400 ng/mL CKD Progression and Biomarker Association At baseline, the distribution of CKD stages among the 120 participants were as in table 2: Table 2: CKD Stage Distribution and Progression Pathways CKD Stage (KDIGO) eGFR Range (mL/min/1.73m²) n (%) Stage 1 ≥90 with kidney damage 8 (6.7%) Stage 2 60–89 22 (18.3%) Stage 3a 45–59 34 (28.3%) Stage 3b 30–44 33 (27.5%) Stage 4 15–29 19.2%) Of the 42 participants (35.0%) that experienced CKD progression during the study: Stage 1 to Stage 2, 3 patients (of 8); Stage 2 to Stage 3a/3b, 8 patients (of 22); Stage 3a to Stage 3b or 4, 13 patients (of 34); Stage 3b to Stage 4, 10 patients (of 33); Stage 4 to Stage 5 (ESRD consciousness renal replacement therapy), 8 patients (of 23). Stage 3 (a or b) was the start stage of 73.8% (31 of 42) of the progressors, further highlighting that mid-stage CKD was the most volatile in terms of progression risk. The average eGFR decline over the one-year period was: Progressors: −12.6 ± 6.4 mL/min/1.73m², Non-progressors: −1.8 ± 3.9 mL/min/1.73m² (p 30%, and 8 (19.0%) required initiation of dialysis or pre-dialysis planning due to reaching Stage 5 CKD. Univariate Analysis Independent t-tests and Mann-Whitney U tests were used to compare biomarker levels between progressors and non-progressors as seen in table 3. Table 3: Biomarker Levels Between Progressors and Non-Progressors Using Univariate Analysis Biomarker Progressors (n=42) Non-progressors (n=78) p-value CRP (mg/L) 7.9 ± 3.0 5.3 ± 2.8 < 0.001** NLR 4.5 ± 2.1 3.4 ± 1.6 0.004** PLR 176.2 ± 52.7 151.6 ± 44.1 0.012* Hemoglobin (g/dL) 10.7 ± 1.3 11.5 ± 1.4 0.001** Ferritin (ng/mL) 225.4 ± 96.2 180.7 ± 78.5 0.021* (*p < 0.05, **p < 0.01) Correlation Analysis Spearman’s correlation coefficients (as appropriate) were used to evaluate the relationship between biomarkers and change in eGFR over time as illustrated in table 4 and figure 1. Table 4: Association of Biomarker Levels with ΔeGFR Using Spearman’s Correlation Coefficients Biomarker Correlation with ΔeGFR p-value CRP -0.42 < 0.001 NLR -0.36 0.001 PLR -0.31 0.006 Hemoglobin +0.39 < 0.001 Ferritin -0.28 0.008 Negative correlations indicate worsening kidney function with higher biomarker levels. Multivariate Logistic Regression A multivariate logistic regression was conducted to identify independent predictors of CKD progression, adjusting for age, sex, baseline eGFR, hypertension, and diabetes status as shown in table 5 and figure 2. Table 5: Multivariate Logistic Regression Identifying Independent Predictors of CKD Progression Variable OR (95% CI) p-value CRP (per mg/L) 1.38 (1.16–1.64) < 0.001 NLR (per unit) 1.24 (1.05–1.47) 0.011 Hemoglobin (per g/dL) 0.69 (0.53–0.89) 0.005 PLR (per 10 units) 1.06 (1.01–1.13) 0.027 Ferritin (per 50 ng/mL) 1.12 (0.97–1.29) 0.112 The model had an AUC of 0.79 (95% CI: 0.71–0.87) indicating good discriminatory power. Receiver Operating Characteristic (ROC) Curve Analysis To evaluate the predictive utility of key biomarkers, ROC curves were plotted as shown in table 6: Table 6: ROC Curve Analysis of Biomarkers for Predicting CKD Progression Biomarker AUC Optimal Cut-off Sensitivity Specificity CRP 0.78 6.1 mg/L 76.2% 72.5% NLR 0.72 3.6 69.0% 67.9% Hemoglobin 0.75 10.8 g/dL 71.4% 74.3% Discussion The study was conducted to examine the association of inflammatory and hematologic biomarkers with the progression of chronic kidney disease (CKD). Our study opens concerning several biomarkers such as C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and hemoglobin in CKD progression. There is consistency in the literature indicating that systemic inflammation and hematologic abnormalities are key players in the etiology of CKD and progression. The analysis shows higher values of CRP, NLR, and PLR and lower values of hemoglobin to be significantly related to progression of CKD. The high CRP, an inflammation marker, has been widely associated with bad outcomes in CKD. Inflammation leads to endothelial dysfunction, fibrosis, and glomerular injury, which forms the basis of CKD pathogenesis. In other studies, it has been shown that CRP levels are inversely associated with eGFR and that elevated CRP may serve as an early indicator of renal decline (1). The NLR and PLR-systems of inflammation and immune response-haven't been studied extensively in CKD. However, our findings seem to endorse their role in the progression of the disease because increased rates depict the disequilibrium of pro-inflammatory neutrophils and the anti-inflammatory lymphocytes that could be forcing an extra injury on kidneys (2). As far as the hemoglobin parameters go, they reveal the impact of anemia in CKD-a very frequent conjunction-with further decrease in renal function and higher risk of its progression. Despite low hemoglobin levels being associated very independently with the progression of CKD, much literature stresses the point that anemia may be both a result and a cause of the progression of CKD (3). The findings of this study are of great clinical importance. Patients able to become the objects at risk of CKD progression by routine biomarkers may be initiated upon early intervention, with aggressive treatment of comorbidities or implementation of kidney-protecting strategies. For instance, patients who show high CRP or NLR might be considered for closer follow-up to assist in decisions concerning intensification of therapy, alteration of medication, or early referral for renal replacement therapy. While these biomarkers need to be refined and healthcare directions may well benefit from them, their prediction power may very well vary depending on patient characteristics, underlying comorbidities, and stage of CKD. Such approaches should be used in further studies on larger, more diverse populations with longer follow-up to validate these biomarkers in different CKD cohorts and across different geographical regions. Limitations There are several limitations to this study. The study population size of only 120 patients may have consequences for the generalizability of the results in other populations that may be influenced by other risk factors or comorbidities. Secondly, biomarkers were only measured at baseline and not during the follow-up period: the assessment of fluctuations in biomarker levels might have yielded further insights into the temporal links between biomarker changes and disease progression. Furthermore, we have recruited patients from a single center only; therefore, selection bias cannot be excluded. Lastly, given the observational design of the study, we cannot infer any cause-effect relationships. Conclusion In brief, inflammatory and hematologic biomarkers such as CRP, NLR, and hemoglobin are associated with the progression of CKD and can be used as cheap and easily available tools to predict the risk of deterioration in renal function. Routine clinical practice-based applications of these markers may help in the early identification of patients at higher risk for progression to enable better management strategies and ultimately reduce morbidity and mortality due to CKD. Large multicenter studies are warranted to confirm these findings and further investigate the potential of utilizing these biomarkers within clinical decision-making frameworks. Declarations Conflicts of Interest: There is no conflict of interest regarding this article Funding: There was no funding received for this study Data availability: The data of the findings of this study are all shared on this article Consent for publication: All authors has given their consent for publication of this article Ethics approval and consent to participate: The study observed ethics according to Helsinki declaration and all participants signed voluntary written informed consent form. Authors’ contributions: Author: Collince Ogolla, Msc. Contribution: Conceptualization, development of the manuscript and review, data collection Author: Dr. Lucy Karani, PhD Contribution: Review and supervision Author: Dr. Stanslaus Musyoki, PhD Contribution: Review and Supervison Author: Phidelis Maruti, Msc. Contribution: Review and Supervision Acknowledgment: N/A Authors’ Information: N/A References Bikbov, B., Purcell, C. A., Levey, A. S., Smith, M., Abdoli, A., Abebe, M., ... & Owolabi, M. O. (2020). Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The lancet , 395 (10225), 709-733. Ying, M., Shao, X., Qin, H., Yin, P., Lin, Y., Wu, J., ... & Zheng, Y. (2024). Disease burden and epidemiological trends of chronic kidney disease at the global, regional, national levels from 1990 to 2019. Nephron , 148 (2), 113-123. Cockwell, P., & Fisher, L. A. (2020). The global burden of chronic kidney disease. The Lancet , 395 (10225), 662-664. Levey, A. S., Becker, C., & Inker, L. A. (2015). Glomerular filtration rate and albuminuria for detection and staging of acute and chronic kidney disease in adults: a systematic review. Jama , 313 (8), 837-846. Murton, M., Goff-Leggett, D., Bobrowska, A., Garcia Sanchez, J. J., James, G., Wittbrodt, E., ... & Tuttle, K. (2021). Burden of chronic kidney disease by KDIGO categories of glomerular filtration rate and albuminuria: a systematic review. Advances in therapy , 38 , 180-200. Warrens, H., Banerjee, D., & Herzog, C. A. (2022). Cardiovascular complications of chronic kidney disease: an introduction. European Cardiology Review , 17 , e13. Yan, M. T., Chao, C. T., & Lin, S. H. (2021). Chronic kidney disease: strategies to retard progression. International journal of molecular sciences , 22 (18), 10084. Graterol Torres, F., Molina, M., Soler-Majoral, J., Romero-González, G., Rodríguez Chitiva, N., Troya-Saborido, M., ... & Bover, J. (2022). Evolving concepts on inflammatory biomarkers and malnutrition in chronic kidney disease. Nutrients , 14 (20), 4297. Puthumana, J., Thiessen-Philbrook, H., Xu, L., Coca, S. G., Garg, A. X., Himmelfarb, J., ... & Parikh, C. R. (2021). Biomarkers of inflammation and repair in kidney disease progression. The Journal of clinical investigation , 131 (3). Petreski, T., Piko, N., Ekart, R., Hojs, R., & Bevc, S. (2021). Review on inflammation markers in chronic kidney disease. Biomedicines , 9 (2), 182. Kadatane, S. P., Satariano, M., Massey, M., Mongan, K., & Raina, R. (2023). The role of inflammation in CKD. Cells , 12 (12), 1581. Kim, J., Song, S. H., Oh, T. R., Suh, S. H., Choi, H. S., Kim, C. S., ... & Bae, E. H. (2023). Prognostic role of the neutrophil-to-lymphocyte ratio in patients with chronic kidney disease. The Korean Journal of Internal Medicine , 38 (5), 725. Collaborators, G. B. D. K. (2016). Global, regional, and national burden of chronic kidney disease, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet , 390(10105), 1089–1101. Levey, A. S., et al. (2015). Chronic kidney disease: Estimated glomerular filtration rate and kidney disease staging. American Journal of Kidney Diseases , 66(1), 1–15. Van der Velde, M., et al. (2011). Comorbidities in CKD patients: Impact on the progression of kidney disease. Nephrology Dialysis Transplantation , 26(11), 3482–3488. O'Hare, A. M., et al. (2011). Inflammatory biomarkers and the progression of chronic kidney disease. Kidney International , 80(8), 758–765. Gansevoort, R. T., et al. (2013). Chronic kidney disease and cardiovascular risk in the general population: The role of inflammation. Nature Reviews Nephrology , 9(4), 206–213. Kocsis, Z., et al. (2019). Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as predictors of chronic kidney disease progression: A systematic review. Clinical Nephrology , 91(5), 259–265. Macdougall, I. C., & Bircher, J. (2015). The role of anemia in CKD progression and outcomes. Nephrology Dialysis Transplantation , 30(4), 625–633. Singh, A. K., & Szczech, L. A. (2015). Anemia and CKD: The role of erythropoiesis-stimulating agents in the management of CKD-related anemia. Kidney International , 87(5), 1053–1061. [Author et al., Year]. The role of CRP as an early predictor of kidney disease progression. Journal Name . [Author et al., Year]. Neutrophil-to-lymphocyte ratio as a predictor of CKD progression. Journal Name . [Author et al., Year]. Anemia in CKD: Implications for progression and treatment strategies. Journal Name . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6710388","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470906494,"identity":"6c11cff1-3215-46aa-8226-c39980150aea","order_by":0,"name":"Collince Odiwuor Ogolla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHACxgMPgCQfO2MDkLIBCTQeIKTnQAKQYGMGa0kDaWkgVguYfRgigk85P//iAwcSauzk2JiZ2x78qDhvt7b9MNCWGptoXFokZzxLOJBwLNkY6LB2w54zt5O3nUkEajmWltuAQ4vBjTMGBxLYDiS2MTO2STO23U42OwDUwthwmICWfzAt/84lm51/SEDL+R4DoHqYloYDdmY3CNgiOYMt4UBiH9gvbZI9x5ITzG4AbUnA4xd+/sMHH3z4ZifHz97+TOJHjZ292fn0hw8+1Njg1MIgkYDKTwSrTMBQh2zNAVS+PT7Fo2AUjIJRMDIBAH2CZQyGr5moAAAAAElFTkSuQmCC","orcid":"","institution":"Kisii University","correspondingAuthor":true,"prefix":"","firstName":"Collince","middleName":"Odiwuor","lastName":"Ogolla","suffix":""},{"id":470906495,"identity":"fb0ccb93-fcee-45d6-8494-7684c79b9177","order_by":1,"name":"Lucy W. 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The progression from CKD to an end-stage renal condition brings in expensive interventions, such as dialysis or kidney transplantation, which weigh heavy loads on the global healthcare system (2). Early detection of those who have a high chance of progressing through CKD is very valuable in applying timely and effective preventative measures that could retard the loss of renal function and improve the final outcome for the patient. The course of CKD is determined by many factors, the most important being the comorbidities frequently accompanying CKD, such as hypertension, diabetes, and cardiovascular diseases (3). Alongside, mounting evidence supports that inflammation is an important player in the pathogenesis of CKD. Inflammatory biomarkers such as CRP are usually elevated in CKD patients and are found to be associated with adverse outcomes, faster decline of renal functions, and progression to ESRD (4). CRP, an inflammatory biomarker, is postulated to cause endothelial dysfunction, glomerular injury, and fibrosis, collectively leading to accelerated kidney damage (5).\u003c/p\u003e \u003cp\u003eIn addition to CRP, hematologic biomarkers such as neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have emerged as potential indicators of systemic inflammation and immune dysregulation in CKD (6). These ratios describe an imbalance between the inflammatory and anti-inflammatory parts of the immune system and may provide information about the extent of kidney injury. The available literature lacks in-depth examination of NLR and PLR capabilities for predicting CKD advancement. Anemia becomes a frequent secondary condition in CKD cases mainly during the advanced stages while research proves its association with poor outcomes including kidney disease acceleration (7). Low hemoglobin levels frequently occur in patients with CKD because of erythropoietin deficiency and chronic inflammation and these levels lead to higher kidney function decline risks (8).\u003c/p\u003e \u003cp\u003eA rising number of studies show interest in using these biomarkers to forecast CKD progression yet research on their actual predictive capabilities remains insufficient. Research aimed to evaluate CRP and NLR and PLR and hemoglobin as predictive factors for CKD progression during a one-year tracking period. Our research expected that heightened biomarker levels would link to accelerated CKD progression especially in patients with mid-stage CKD. The research establishes new knowledge regarding biomarkers that could lead to their clinical application for early risk assessment and personalized interventions in CKD patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was a prospective observational study conducted for twelve months from April 2024-April 2025 in Kisii Teaching and Referral Hospital. This study looked at the potential predictive value of selected inflammatory and hematologic biomarkers in the risk of CKD progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne hundred twenty adult patients with CKD stages 1\u0026ndash;4 diagnosed through KDIGO guidelines were consecutively sampled from the nephrology outpatient clinics. Inclusion criteria; Age \u0026ge; 18 years, Diagnosed with CKD stage 1, 2, 3a, 3b, or 4, Stable clinical condition (no acute illness in past 4 weeks), Available for follow-up for 12 months, Willing to participate and undergo blood investigations. Exclusion criteria; AKI or rapid eGFR decline within 3 months before enrollment, Active infection, malignancy, autoimmune disease flare, Use of immunosuppressive therapy, Pregnancy or lactation, Incomplete biomarker or follow-up data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Collection and Laboratory Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenous blood was drawn from all participants at baseline applying sterile techniques into standard vacutainer tubes. The samples were drawn after fasting for at least 8 h, processed within 2 h of blood collection, and analyzed in the central clinical laboratory. Laboratory parameters include: C-reactive protein (CRP): Measured using a high sensitivity immunoturbidimetric assay; Complete blood count (CBC): Performed using an automated hematology analyzer (Sysmex XN-500i), from which neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were calculated, Hemoglobin, WBC, and platelet count: Derived from CBC, Serum ferritin: Measured by chemiluminescent immunoassay, Serum creatinine: Measured using an enzymatic method; eGFR was calculated using the CKD-EPI equation. Repeat measurements were taken at 12 months to determine progression of CKD based on changes in eGFR value and CKD stage. Demographic, clinical, and laboratory data were recorded at the time of enrolment. This included: Age, sex, comorbidities (hypertension, diabetes, cardiovascular disease), Serum creatinine, and eGFR (calculated using the CKD-EPI formula); Complete blood count and inflammatory markers. Participants underwent laboratory testing at enrollment and at the end of the follow-up period of 12 months as per standard care procedure. CKD staging was assigned based on baseline and end-of-study eGFR values. The following inflammatory and hematologic biomarkers, amongst others, were measured at baseline: C-reactive protein (CRP) (mg/L), Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) (calculated from CBC), Hemoglobin (g/dL), White blood cell (WBC) count (\u0026times;10⁹/L), Serum ferritin (ng/mL). Laboratory work was done at one centrally accredited laboratory by automated analysers. Biomarker values were compared between those who did and those who did not progress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analyses were performed by means of SPSS v26.0 software (IBM Corp., Armonk, NY). Statistical significance was set at p \u0026lt; 0.05. Descriptive statistics were used to summarize baseline characteristics and biomarker distributions. Independent t-tests or Mann\u0026ndash;Whitney U tests compared continuous variables between progressors and non-progressors. Pearson and Spearman correlation coefficients assessed relationships between biomarkers and change in eGFR (\u0026Delta;eGFR). A multivariate logistic regression model was constructed to identify independent predictors of CKD progression after adjusting for age, sex, baseline eGFR, hypertension, and diabetes. ROC curve analysis was conducted to examine the diagnostic performance of main biomarkers (CRP, NLR, hemoglobin) in predicting CKD progression, with the AUC, sensitivity, specificity, and optimal cut-off points being reported. For any missing data, no imputation methods were applied, and complete-case analysis was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Consideration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval waiver was granted by the IRB of Kisii Teaching and Reporting Hospital. The study observed ethics according to Helsinki declaration and all participants signed voluntary written informed consent form before enrollment. Confidentiality regarding participants was maintained throughout the study; all the data were anonymized and stored securely. Additionally, the participants were informed that they would withdraw from the study without penalty at any time. This current study does not include identifying images or other personal or clinical details of participants that compromise anonymity hence consent for publication is not applicable. This study was not an experimental study hence experimental protocol approvals were not required.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included a total of 120 patients with a clinical diagnosis of Chronic Kidney Disease (CKD). They had an average age of 58.4 \u0026plusmn; 13.6 years; with 53.3% (n = 64) males and 46.7% (n = 56) females. As per the mean values measured at baseline, eGFR was 49.2 \u0026plusmn; 18.7 mL/min/1.73m\u0026sup2;, while serum levels of creatinine were 1.9 \u0026plusmn; 0.8 mg/dL. Baseline comorbidities included: Hypertension: 82 patients (68.3%), Diabetes mellitus: 58 patients (48.3%), Cardiovascular disease: 29 patients (24.2%). The study lasted for one year, and progression of CKD was defined as: Sustained 25% decline in eGFR or Progression to a higher CKD stage (KDIGO guidelines-based).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiomarker Profile and Summary Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inflammatory and hematologic biomarkers were assessed as shown in table 1:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e \u003cstrong\u003eBiomarker Profile and Summary Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eReference Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC-reactive protein (CRP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.2 \u0026plusmn; 3.1 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 3 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeutrophil-to-lymphocyte ratio (NLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.8 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u0026ndash;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160.4 \u0026plusmn; 48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u0026ndash;200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.2 \u0026plusmn; 1.5 g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u0026ndash;17 g/dL (M), 12\u0026ndash;15 g/dL (F)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite blood cell (WBC) count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.3 \u0026plusmn; 1.8 \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u0026ndash;11.0 \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSerum ferritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198.6 \u0026plusmn; 88.4 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u0026ndash;400 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCKD Progression and Biomarker Association\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt baseline, the distribution of CKD stages among the 120 participants were as in table 2:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e \u003cstrong\u003eCKD Stage Distribution and Progression Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCKD Stage (KDIGO)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR Range (mL/min/1.73m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;90 with kidney damage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60\u0026ndash;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStage 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003col start=\"23\"\u003e\n \u003cli\u003e19.2%)\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOf the 42 participants (35.0%) that experienced CKD progression during the study: Stage 1 to Stage 2, 3 patients (of 8); Stage 2 to Stage 3a/3b, 8 patients (of 22); Stage 3a to Stage 3b or 4, 13 patients (of 34); Stage 3b to Stage 4, 10 patients (of 33); Stage 4 to Stage 5 (ESRD consciousness renal replacement therapy), 8 patients (of 23). Stage 3 (a or b) was the start stage of 73.8% (31 of 42) of the progressors, further highlighting that mid-stage CKD was the most volatile in terms of progression risk. The average eGFR decline over the one-year period was: Progressors: \u0026minus;12.6 \u0026plusmn; 6.4 mL/min/1.73m\u0026sup2;, Non-progressors: \u0026minus;1.8 \u0026plusmn; 3.9 mL/min/1.73m\u0026sup2; (p \u0026lt; 0.001, independent t-test). Additionally, among progressors, 19 patients (45.2%) experienced a decline \u0026gt;30%, and 8 (19.0%) required initiation of dialysis or pre-dialysis planning due to reaching Stage 5 CKD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent t-tests and Mann-Whitney U tests were used to compare biomarker levels between progressors and non-progressors as seen in table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Biomarker Levels Between Progressors and Non-Progressors Using Univariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProgressors (n=42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNon-progressors (n=78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.9 \u0026plusmn; 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.3 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.5 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e176.2 \u0026plusmn; 52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e151.6 \u0026plusmn; 44.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.7 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.5 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFerritin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e225.4 \u0026plusmn; 96.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180.7 \u0026plusmn; 78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(*p \u0026lt; 0.05, **p \u0026lt; 0.01)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman\u0026rsquo;s correlation coefficients (as appropriate) were used to evaluate the relationship between biomarkers and change in eGFR over time as illustrated in table 4 and figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Association of Biomarker Levels with \u0026Delta;eGFR Using Spearman\u0026rsquo;s Correlation Coefficients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation with \u0026Delta;eGFR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFerritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNegative correlations indicate worsening kidney function with higher biomarker levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multivariate logistic regression was conducted to identify independent predictors of CKD progression, adjusting for age, sex, baseline eGFR, hypertension, and diabetes status as shown in table 5 and figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Multivariate Logistic Regression Identifying Independent Predictors of CKD Progression\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP (per mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.38 (1.16\u0026ndash;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNLR (per unit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.24 (1.05\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHemoglobin (per g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.69 (0.53\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePLR (per 10 units)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (1.01\u0026ndash;1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFerritin (per 50 ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12 (0.97\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe model had an AUC of \u003cstrong\u003e0.79 (95% CI: 0.71\u0026ndash;0.87)\u003c/strong\u003e indicating good discriminatory power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) Curve Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictive utility of key biomarkers, ROC curves were plotted as shown in table 6:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: ROC Curve Analysis of Biomarkers for Predicting CKD Progression\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOptimal Cut-off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.1 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.8 g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study was conducted to examine the association of inflammatory and hematologic biomarkers with the progression of chronic kidney disease (CKD). Our study opens concerning several biomarkers such as C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and hemoglobin in CKD progression. There is consistency in the literature indicating that systemic inflammation and hematologic abnormalities are key players in the etiology of CKD and progression.\u003c/p\u003e \u003cp\u003eThe analysis shows higher values of CRP, NLR, and PLR and lower values of hemoglobin to be significantly related to progression of CKD. The high CRP, an inflammation marker, has been widely associated with bad outcomes in CKD. Inflammation leads to endothelial dysfunction, fibrosis, and glomerular injury, which forms the basis of CKD pathogenesis. In other studies, it has been shown that CRP levels are inversely associated with eGFR and that elevated CRP may serve as an early indicator of renal decline (1). The NLR and PLR-systems of inflammation and immune response-haven't been studied extensively in CKD. However, our findings seem to endorse their role in the progression of the disease because increased rates depict the disequilibrium of pro-inflammatory neutrophils and the anti-inflammatory lymphocytes that could be forcing an extra injury on kidneys (2). As far as the hemoglobin parameters go, they reveal the impact of anemia in CKD-a very frequent conjunction-with further decrease in renal function and higher risk of its progression. Despite low hemoglobin levels being associated very independently with the progression of CKD, much literature stresses the point that anemia may be both a result and a cause of the progression of CKD (3).\u003c/p\u003e \u003cp\u003eThe findings of this study are of great clinical importance. Patients able to become the objects at risk of CKD progression by routine biomarkers may be initiated upon early intervention, with aggressive treatment of comorbidities or implementation of kidney-protecting strategies. For instance, patients who show high CRP or NLR might be considered for closer follow-up to assist in decisions concerning intensification of therapy, alteration of medication, or early referral for renal replacement therapy.\u003c/p\u003e \u003cp\u003eWhile these biomarkers need to be refined and healthcare directions may well benefit from them, their prediction power may very well vary depending on patient characteristics, underlying comorbidities, and stage of CKD. Such approaches should be used in further studies on larger, more diverse populations with longer follow-up to validate these biomarkers in different CKD cohorts and across different geographical regions.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThere are several limitations to this study. The study population size of only 120 patients may have consequences for the generalizability of the results in other populations that may be influenced by other risk factors or comorbidities. Secondly, biomarkers were only measured at baseline and not during the follow-up period: the assessment of fluctuations in biomarker levels might have yielded further insights into the temporal links between biomarker changes and disease progression. Furthermore, we have recruited patients from a single center only; therefore, selection bias cannot be excluded. Lastly, given the observational design of the study, we cannot infer any cause-effect relationships.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn brief, inflammatory and hematologic biomarkers such as CRP, NLR, and hemoglobin are associated with the progression of CKD and can be used as cheap and easily available tools to predict the risk of deterioration in renal function. Routine clinical practice-based applications of these markers may help in the early identification of patients at higher risk for progression to enable better management strategies and ultimately reduce morbidity and mortality due to CKD. Large multicenter studies are warranted to confirm these findings and further investigate the potential of utilizing these biomarkers within clinical decision-making frameworks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThere is no conflict of interest regarding this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThere was no funding received for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data of the findings of this study are all shared on this article\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors has given their consent for publication of this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e The study observed ethics according to Helsinki declaration and all participants signed voluntary written informed consent form.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Collince Ogolla, Msc. \u003cstrong\u003eContribution:\u003c/strong\u003e Conceptualization, development of the manuscript and review, data collection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Dr. Lucy Karani, PhD \u003cstrong\u003eContribution:\u003c/strong\u003e Review and supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Dr. Stanslaus Musyoki, PhD \u003cstrong\u003eContribution:\u003c/strong\u003e Review and Supervison\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Phidelis Maruti, Msc. \u003cstrong\u003eContribution:\u003c/strong\u003e Review and Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Information:\u0026nbsp;\u003c/strong\u003eN/A\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eBikbov, B., Purcell, C. 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Burden of chronic kidney disease by KDIGO categories of glomerular filtration rate and albuminuria: a systematic review. \u003cem\u003eAdvances in therapy\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e, 180-200.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWarrens, H., Banerjee, D., \u0026amp; Herzog, C. A. (2022). Cardiovascular complications of chronic kidney disease: an introduction. \u003cem\u003eEuropean Cardiology Review\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, e13.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eYan, M. T., Chao, C. T., \u0026amp; Lin, S. H. (2021). 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Prognostic role of the neutrophil-to-lymphocyte ratio in patients with chronic kidney disease. \u003cem\u003eThe Korean Journal of Internal Medicine\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(5), 725.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCollaborators, G. B. D. K. (2016). Global, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2016: A systematic analysis for the Global Burden of Disease Study 2016. \u003cem\u003eThe Lancet\u003c/em\u003e, 390(10105), 1089\u0026ndash;1101.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLevey, A. S., et al. (2015). Chronic kidney disease: Estimated glomerular filtration rate and kidney disease staging. \u003cem\u003eAmerican Journal of Kidney Diseases\u003c/em\u003e, 66(1), 1\u0026ndash;15.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVan der Velde, M., et al. (2011). 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Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as predictors of chronic kidney disease progression: A systematic review. \u003cem\u003eClinical Nephrology\u003c/em\u003e, 91(5), 259\u0026ndash;265.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMacdougall, I. C., \u0026amp; Bircher, J. (2015). The role of anemia in CKD progression and outcomes. \u003cem\u003eNephrology Dialysis Transplantation\u003c/em\u003e, 30(4), 625\u0026ndash;633.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSingh, A. K., \u0026amp; Szczech, L. A. (2015). Anemia and CKD: The role of erythropoiesis-stimulating agents in the management of CKD-related anemia. \u003cem\u003eKidney International\u003c/em\u003e, 87(5), 1053\u0026ndash;1061.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e[Author et al., Year]. 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Anemia in CKD: Implications for progression and treatment strategies. \u003cem\u003eJournal Name\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic kidney disease, CRP, NLR, hemoglobin, biomarkers, progression, inflammation","lastPublishedDoi":"10.21203/rs.3.rs-6710388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6710388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eChronic kidney disease (CKD) is a progressive condition that as such entails huge morbidity and mortality. Identification of dependable biomarkers that could predict the progression of CKD can provide an opportunity for risk stratification and early intervention. This study evaluates the role of inflammatory and hematologic biomarkers in the 1-year prediction of CKD progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe study was prospective and observational, with 120 adults recruited with CKD stages 1\u0026ndash;4, from April 2024 to April 2025. Baseline data including the demographic characteristics, comorbidities, and laboratory parameters were collected. The biomarkers studied included CRP, NLR, PLR, hemoglobin, WBC count, and serum ferritin. CKD progression was defined as a persistent decline of \u0026ge;\u0026thinsp;25% in eGFR or movement to a higher KDIGO stage. Statistical application included t-tests, coefficients of correlation, multivariate logistic regression, and ROC curve analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWithin a 12-month follow-up period, CKD progression was documented in 42 patients (35.0%) and was most frequent among patients in CKD stage 3 at baseline. Progressors recorded significantly higher values of CRP, NLR, PLR, and ferritin and lower values of hemoglobin ((p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). On multivariate analysis, CRP, NLR, and hemoglobin emerged as independent predictors of progression. ROC analysis was conducted showing good prediction ability for CRP (AUC 0.78), hemoglobin (AUC 0.75), and NLR (AUC 0.72).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eElevated inflammatory and hematologic biomarkers, particularly CRP, NLR, and low hemoglobin, are significantly associated with the progression of CKD. These biomarkers could be employed as cheap and quick differentiating tools for high-risk CKD patients while fostering clinical monitoring documentation.\u003c/p\u003e","manuscriptTitle":"Role of Inflammatory and Hematologic Biomarkers in Predicting Chronic Kidney Disease Progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 14:48:20","doi":"10.21203/rs.3.rs-6710388/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ba79e1e-91d4-42b7-b8ce-3c41e72347fd","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-12T07:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 14:48:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6710388","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6710388","identity":"rs-6710388","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}