Association between CBC-derived inflammatory indices and radiographic severity in knee osteoarthritis: A retrospective cohort study

Abstract

Objectives

This study aimed to evaluate hematological inflammatory indices in patients with knee osteoarthritis and to investigate their association with radiographic disease severity. It also assessed whether these indices may have adjunctive value in distinguishing early-stage from late-stage osteoarthritis.

Methods

This retrospective study included 2176 patients diagnosed with knee osteoarthritis between January 1, 2023, and July 1, 2025. Patients aged 50–90 years with primary knee osteoarthritis and an available complete blood count (CBC) were included. Age, gender, Kellgren–Lawrence (KL) stage, neutrophil, lymphocyte, monocyte, and platelet counts were recorded. Neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), systemic immune inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune inflammation value (PIV) were calculated. Receiver operating characteristic (ROC) analysis and multivariable binary logistic regression adjusted for age and gender were performed.

Results

NLR (p < 0.001), MLR (p < 0.001), SII (p < 0.001), SIRI (p < 0.001), and PIV (p < 0.001) were significantly higher in patients with late-stage osteoarthritis, whereas PLR did not differ significantly between groups (p = 0.061). ROC analysis showed that NLR, MLR, SII, SIRI, and PIV had statistically significant but poor to modest discriminatory ability for late-stage osteoarthritis. Although SIRI showed the highest AUC among the evaluated indices, its performance remained within the modest range, and none of the markers demonstrated clinically meaningful discriminatory ability. In multivariable binary logistic regression analysis adjusted for age and gender, NLR, MLR, SII, SIRI, and PIV remained associated after adjustment for age and gender with late-stage osteoarthritis.

Conclusion

NLR, MLR, SII, SIRI, and PIV were associated with greater radiographic severity in knee osteoarthritis. Among the investigated biomarkers, SIRI, PIV, and SII showed relatively better discriminatory performance than the other indices; however, the overall ability of all markers to distinguish early-stage from late-stage osteoarthritis was limited. These findings suggest that CBC-derived inflammatory indices may serve as inexpensive adjunctive markers of radiographic severity, but they should not be used as stand-alone tools in clinical decision-making.

Keywords

knee osteoarthritis systemic immune inflammation index systemic immune response index pan-immune inflammation value radiographic severity

Highlights

• Inflammatory indices were associated with radiographic severity in knee OA

• SIRI showed the highest discriminatory performance among evaluated indices

• Composite indices performed better than simple inflammatory ratios

• Overall diagnostic performance of these markers was limited

• CBC-derived indices may serve as adjunctive inflammatory markers

Introduction

Osteoarthritis is a progressive and multifactorial joint disease characterized by cartilage degeneration, subchondral bone remodeling, osteophyte formation, and varying degrees of synovial inflammation.¹ It is the most common joint disorder worldwide and a major cause of pain, functional limitation, and reduced quality of life, particularly in older adults.² The knee is one of the most frequently affected joints, and knee osteoarthritis represents a substantial clinical and socioeconomic burden.^3,4

Although knee osteoarthritis has traditionally been regarded as a primarily degenerative condition, increasing evidence suggests that chronic low-grade inflammation contributes to both symptom burden and structural progression.⁵ Inflammatory cytokines, oxidative stress, and matrix-degrading enzymes are involved in cartilage damage, synovial changes, and subchondral bone alterations.⁶ Therefore, biomarkers reflecting systemic inflammatory burden may provide additional insight into disease severity.⁷

Complete blood count–derived inflammatory indices have attracted growing interest because they are inexpensive, widely available, and easily calculated in routine clinical practice.⁸ Among these, the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to lymphocyte ratio (MLR) are relatively simple indices, whereas the systemic immune inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune inflammation value (PIV) may provide a more integrated reflection of systemic inflammatory activity.⁹ These indices have been investigated as accessible biomarkers of inflammatory burden and disease severity in various chronic inflammatory and degenerative conditions.¹⁰

In knee osteoarthritis, previous studies have mainly focused on simpler indices such as NLR, PLR, and MLR, and the available evidence remains inconsistent. Data on SII, SIRI, and especially PIV in relation to radiographic severity of knee osteoarthritis remain limited. Clarifying the relationship between these hematological inflammatory indices and radiographic stage may help define their potential adjunctive value in clinical assessment.

Although several studies have investigated simple inflammatory indices such as NLR, PLR, and MLR in knee osteoarthritis, data regarding more comprehensive indices such as SII, SIRI, and especially PIV in relation to radiographic severity are limited. Moreover, studies with large sample sizes evaluating these indices simultaneously are lacking.

The aims of our study were to evaluate the levels of various inflammatory indices in individuals with knee osteoarthritis, to better understand the inflammatory component of the disease, and to investigate the association between these indices and radiographic severity. This study also aimed to assess whether these indices may have potential adjunctive value in distinguishing early-stage from late-stage osteoarthritis. To our knowledge, this is the first large-scale study evaluating SII, SIRI, and PIV simultaneously in relation to radiographic severity of knee osteoarthritis.

Materials and methods

This retrospective study included 2176 patients diagnosed with knee osteoarthritis between January 1, 2023, and July 1, 2025, identified through the hospital electronic archive system. The study was approved by the local ethics committee, and the manuscript does not contain any patient-identifying information.

Patients aged 50–90 years with primary knee osteoarthritis and an available CBC were included. Exclusion criteria were a history of bilateral lower-extremity surgery, septic arthritis, intra-articular injections, rheumatologic disease, metabolic bone disease, and malignancy. Patients with secondary causes of osteoarthritis or conditions likely to substantially influence systemic inflammatory markers were not included.

For all included patients, age, gender, and Kellgren–Lawrence (KL) stage were recorded. Grades 1 and 2 were classified as early-stage osteoarthritis, whereas grades 3 and 4 were classified as late-stage osteoarthritis. Neutrophil, lymphocyte, monocyte, and platelet counts were extracted from laboratory records, and the following indices were calculated: neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), systemic immune inflammation index (SII; platelet × neutrophil/lymphocyte), systemic inflammation response index (SIRI; neutrophil × monocyte/lymphocyte), and pan-immune inflammation value (PIV; neutrophil × monocyte × platelet/lymphocyte).

Radiographic grading was performed according to the KL classification using standard anteroposterior knee radiographs retrieved from the hospital archive. Grading was performed by one orthopedic surgeon who was blinded to laboratory data. CBC parameters obtained at the same time of clinical evaluation were used for analysis.

Statistical analysis

IBM SPSS Statistics 28 (IBM Corporation, Armonk, NY, USA) was used for statistical analysis. Descriptive statistics were presented as number (n), percentage (%), mean ± standard deviation, or median (minimum–maximum), as appropriate. The Kolmogorov–Smirnov test was used to assess the normality of data distribution. For comparisons between the early-stage and late-stage osteoarthritis groups, the independent samples t-test was used for normally distributed continuous variables, and the Mann–Whitney U test was used for non-normally distributed continuous variables. Categorical variables were compared using the chi-square test.

Receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of inflammatory biomarkers to discriminate between early-stage and late-stage osteoarthritis. The optimal cut-off value for each biomarker was determined using the Youden J index; however, these cut-off values should be considered exploratory and specific to this study population, and sensitivity, specificity, area under the curve (AUC), and 95% confidence intervals were calculated. To determine whether hematological inflammatory indices were independently associated with late-stage osteoarthritis, multivariable binary logistic regression analysis was performed. Late-stage osteoarthritis was entered as the dependent variable. Each inflammatory index (NLR, MLR, SII, SIRI, and PIV) was evaluated in separate models to minimize potential multicollinearity arising from overlapping hematological parameters. Because these indices are derived from shared components (neutrophils, lymphocytes, monocytes, and platelets), including them simultaneously in a single model may lead to unstable estimates and unreliable coefficient interpretation.

Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. A p value of <0.05 was considered statistically significant.

Age and gender were included as adjustment variables. However, important potential confounders such as body mass index, smoking status, metabolic comorbidities, and medication use were not available in the dataset and could not be included in the models. Therefore, the regression analyses should be interpreted as partially adjusted models rather than fully adjusted models. The reported associations cannot be considered independent in a strict causal inference framework, and residual confounding is likely to be substantial.

Each inflammatory index (NLR, MLR, SII, SIRI, and PIV) was evaluated in separate models to minimize potential multicollinearity arising from shared hematological components.

Results

A total of 2176 patients with knee osteoarthritis were included in the study. Of these, 1275 (58.6%) were female and 901 (41.4%) were male. According to the Kellgren–Lawrence classification, 389 patients were grade 1, 499 were grade 2, 677 were grade 3, and 611 were grade 4. Overall, 888 patients were classified as having early-stage osteoarthritis (KL grades 1–2), and 1288 were classified as having late-stage osteoarthritis (KL grades 3–4). Descriptive characteristics of the study population are presented in Table 1.

Table 1.

Baseline characteristics of the study population.

Number of patients	2176
Age (years)	61.42 ± 6.34
Gender, n (%)
Female	1275 (58.6%)
Male	901 (41.4%)
KL grade, n (%)
Grade 1	389 (17.9%)
Grade 2	499 (22.9%)
Grade 3	677 (31.1%)
Grade 4	611 (28.1%)
OA stage, n (%)
Early-stage (KL 1-2)	888 (40.8%)
Late-stage (KL 3-4)	1288 (59.2%)

Values are presented as number (n) and percentage (%), or mean ± standard deviation. KL: Kellgren-Lawrence; OA: osteoarthritis.

When the early-stage and late-stage osteoarthritis groups were compared, age did not differ significantly between the groups (p = 0.08), whereas gender distribution differed significantly (p < 0.001). Neutrophil, lymphocyte, monocyte, and platelet counts were significantly higher in the late-stage group. Among the calculated hematological inflammatory indices, NLR, MLR, SII, SIRI, and PIV were significantly higher in patients with late-stage osteoarthritis, whereas PLR did not differ significantly between groups (p = 0.061). Detailed comparisons between groups are shown in Table 2.

Table 2.

Comparison of patients according to osteoarthritis stage.

	Early-stage osteoarthritis group (KL grades 1 and 2)	Late-stage osteoarthritis group (KL grades 3 and 4)	p value
	N = 888	N = 1288	p value
Age (years)	60.42 ± 3.98	62.09 ± 7.44	0.08
Gender, female/male	601/287	674/614	<0.001
Neutrophil (×10⁹/L)	4.3 ± 0.63	5.05 ± 0.59	<0.001
Lymphocyte (×10⁹/L)	2.16 ± 0.48	2.3 ± 0.35	0.003
Monocyte (×10⁹/L)	0.41 ± 0.08	0.45 ± 0.08	<0.001
Platelet (×10⁹/L)	263.42 ± 60.81	280.2 ± 64.65	0.020
NLR	2.06 ± 0.43	2.25 ± 0.42	<0.001
PLR	127.96 ± 42.01	124.93 ± 34.9	0.061
MLR	0.2 ± 0.04	0.23 ± 0.04	<0.001
SII	543.3 ± 177.04	632.97 ± 210.19	<0.001
SIRI	0.86 ± 0.22	1.03 ± 0.25	<0.001
PIV	227.94 ± 92.01	291.67 ± 111.57	<0.001

KL: Kellgren Lawrence; NLR: neutrophil to lymphocyte count ratio; PLR: platelet to lymphocyte count ratio; MLR: monocyte to lymphocyte count ratio; SII: systemic immune inflammation index; SIRI: systemic inflammation response index; PIV: pan-immune inflammation value.

ROC analysis was performed to evaluate the ability of hematological inflammatory indices to distinguish early-stage from late-stage osteoarthritis. Cut-off values, AUCs, sensitivity, specificity, and p values for each index are shown in Table 3. NLR, MLR, SII, SIRI, and PIV were statistically associated with late-stage osteoarthritis; however, their discriminatory performance was poor to modest overall, indicating limited ability to distinguish between early-stage and late-stage osteoarthritis.

Table 3.

ROC analysis of inflammatory indices for distinguishing early-stage and late-stage knee osteoarthritis.

	Cut-off	Sensitivity	Specificity	AUC (95%CI)	p-value
NLR	1.92	79	45	0.621 (0.598–0.645)	<0.001
PLR	92.56	84	27	0.477 (0.452–0.502)	0.061
MLR	0.20	60	57	0.548 (0.523–0.573)	<0.001
SII	612.6	55	69	0.621 (0.597–0.645)	<0.001
SIRI	1.04	56	78	0.688 (0.666–0.710)	<0.001
PIV	216.7	80	62	0.678 (0.655–0.701)	<0.001

AUC: area under curve; CI: confidence interval; NLR: neutrophil to lymphocyte count ratio; PLR: platelet to lymphocyte count ratio; MLR: monocyte to lymphocyte count ratio; SII: systemic immune inflammation index; SIRI: systemic inflammation response index; PIV: pan-immune inflammation value.

Multivariable binary logistic regression analysis adjusted for age and gender demonstrated that NLR, MLR, SII, SIRI, and PIV were significantly associated with late-stage knee osteoarthritis after adjustment for age and gender. Among the evaluated indices, SIRI showed the strongest independent association with late-stage disease (OR 1.41, 95% CI 1.12–1.79, p < 0.001), followed by PIV (OR 1.36, 95% CI 1.06–1.72, p = 0.002) and SII (OR 1.32, 95% CI 1.05–1.78, p = 0.002). Detailed results of the multivariable models are presented in Table 4.

Table 4.

Multivariable binary logistic regression models for factors associated with late-stage knee osteoarthritis.

Model	Age, adjusted OR (95%CI)	Male sex, adjusted OR (95% CI)	Hematological index, adjusted OR (95% CI)	p value for hematological index
NLR	0.94 (0.82−1.01)	1.12 (0.98−1.31)	1.28 (1.03−1.62)	0.003
MLR	0.92 (0.84−1.04)	1.14 (0.96−1.29)	1.26 (1.02−1.64)	0.040
SII	0.94 (0.81−1.02)	1.17 (1.01−1.32)	1.32 (1.05−1.78)	0.002
SIRI	0.93 (0.88−1.04)	1.19 (1.05−1.34)	1.41 (1.12−1.79)	<0.001
PIV	0.96 (0.85−1.03)	1.15 (1.02−1.28)	1.36 (1.06−1.72)	0.002

OR, odds ratio; CI, confidence interval; NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; SII, systemic immune inflammation index; SIRI, systemic inflammation response index; PIV, pan-immune inflammation value.

Discussion

The present study demonstrated that NLR, MLR, SII, SIRI, and PIV were associated with greater radiographic severity in knee osteoarthritis, whereas PLR was not. Although these biomarkers showed statistically significant associations with late-stage disease, their overall discriminatory performance was limited. Although SIRI showed the highest AUC among the evaluated indices, its performance remained within the modest range and did not reach a level that would be considered clinically useful. Similar considerations apply to the other indices. In addition, these associations remained significant after adjustment for age and gender, suggesting that they were not explained solely by basic demographic differences between groups.

Growing evidence suggests that osteoarthritis is not only a degenerative disorder but also a condition influenced by chronic low-grade inflammation. Inflammatory mediators may contribute to cartilage degeneration, synovial changes, and structural progression, which has increased interest in inexpensive and accessible biomarkers derived from routine blood tests. In this context, CBC–derived inflammatory indices may provide indirect information about systemic inflammatory burden in patients with knee osteoarthritis.^11–13

Several studies have evaluated complete blood count–derived inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) in patients with osteoarthritis. Recent systematic reviews and meta-analyses have shown that NLR levels are significantly higher in patients with osteoarthritis compared to healthy controls, supporting the role of systemic inflammation in osteoarthritis pathogenesis.¹⁴ However, the relationship between NLR and radiographic severity remains controversial, and some studies have reported that NLR does not significantly correlate with disease severity despite being elevated in osteoarthritis patients.^14–18 In addition, clinical studies evaluating hematological inflammatory markers demonstrated that although NLR and MLR levels were associated with radiographic osteoarthritis grades, their diagnostic performance was limited in ROC analyses.¹⁵ In our cohort, both NLR and MLR were significantly higher in late-stage disease and remained associated after adjustment for age and gender with late-stage osteoarthritis after adjustment for age and gender. However, their ROC performance was limited, indicating that these indices are unlikely to be useful as stand-alone markers for stage discrimination. Additionally, these cut-off values may not be clinically applicable due to low specificity/sensitivity balance.

In recent years, composite inflammatory indices such as systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV) have gained attention as markers reflecting systemic inflammatory burden. Population-based studies using large datasets such as NHANES demonstrated that elevated SII levels were significantly associated with a higher prevalence of osteoarthritis.¹⁹ Similarly, cross-sectional studies reported significant associations between SII, SIRI, and osteoarthritis risk, suggesting that systemic inflammation may contribute to disease development and progression.^20,21 These composite indices include neutrophils, lymphocytes, monocytes, and platelets, which represent both inflammatory and immune responses, and therefore may better reflect systemic inflammatory status compared to single inflammatory ratios. Furthermore, recent studies have suggested that SII may also be associated with mortality risk and systemic inflammatory burden in patients with osteoarthritis.²² However, studies evaluating SII in relation to radiographic severity reported that although SII values were higher in severe osteoarthritis, its predictive value for disease severity remained limited.²³ In our study compared with NLR and MLR, the composite indices SII, SIRI, and PIV showed relatively better discriminatory performance. This may be because these indices incorporate multiple hematological parameters and may therefore reflect systemic inflammatory burden more comprehensively than simpler ratios. In particular, SIRI showed the highest discriminatory performance among the investigated indices, followed by PIV and SII. Nevertheless, the overall AUC values remained modest, and these markers should be interpreted as adjunctive rather than definitive tools for distinguishing disease stage.

Although SII, SIRI, and PIV showed relatively higher AUC values compared to NLR and MLR, the overall discriminatory performance of all indices remained in the poor to modest range, indicating limited clinical applicability for disease stage differentiation. Similar findings have been reported in previous studies evaluating inflammatory markers in osteoarthritis, where hematological inflammatory indices were associated with disease presence but showed insufficient diagnostic accuracy when used alone.^15,23 This may be explained by the multifactorial nature of osteoarthritis, in which mechanical stress, aging, obesity, metabolic factors, and inflammation all contribute to disease progression rather than inflammation alone.²⁴ Therefore, inflammatory indices may reflect systemic inflammatory burden but may not directly correspond to radiographic severity alone. Recent biomarker studies also emphasize that no single biomarker is sufficient to determine osteoarthritis severity, and multimodal assessment combining clinical, radiographic, and biochemical markers is recommended.²⁵ These findings support the interpretation that CBC-derived inflammatory indices should be considered supportive inflammatory markers rather than standalone diagnostic tools for determining radiographic severity. Importantly, statistical significance in ROC analysis should not be interpreted as clinical utility. Despite achieving statistical significance, the AUC values of all investigated indices remained in the poor to modest range, indicating limited practical value for distinguishing disease stages. In practical terms, these findings suggest that these biomarkers are unlikely to be useful as standalone diagnostic tools for stage differentiation.

An additional important finding of this study is that the associations of NLR, MLR, SII, SIRI, and PIV with late-stage osteoarthritis remained significant after adjustment for age and gender. This suggests that the observed relationships were not explained solely by age and gender differences between groups. However, because potentially relevant confounders such as body mass index, smoking status, metabolic comorbidities, medication use, and other subclinical inflammatory conditions were not available in the electronic records, residual confounding is likely to be substantial.

From a clinical perspective, the present findings suggest that CBC-derived inflammatory indices may have value as inexpensive and readily obtainable adjunctive markers of radiographic severity. However, because their discriminatory performance was limited, these indices should not be used in isolation for clinical decision-making or disease staging. Rather, they may be considered supportive markers that complement radiographic and clinical assessment.

In the present study, each inflammatory index was evaluated in separate regression models due to concerns regarding multicollinearity, as these indices are calculated from overlapping hematological parameters. However, this approach limits direct comparison of effect sizes across indices and does not fully resolve collinearity-related issues and may result in unstable or biased estimates. Consequently, the estimated associations may be unstable and should be interpreted with caution.

More robust statistical approaches, such as including variables within a single model with formal collinearity diagnostics or applying penalized regression techniques, would provide more reliable and comparable estimates and should be considered in future studies. Therefore, the relative superiority of one index over another in this study should be interpreted cautiously.

In addition to musculoskeletal conditions, CBC-derived inflammatory indices such as SII, SIRI, and PIV have been widely investigated in cardiovascular diseases, where they have demonstrated prognostic and diagnostic value as markers of systemic inflammation. Recent studies have shown that elevated levels of these indices are associated with adverse cardiovascular outcomes and reflect underlying inflammatory and immune dysregulation. This broader body of evidence supports the concept that these indices are not disease-specific but rather represent integrative biomarkers of systemic inflammatory burden. Therefore, their association with osteoarthritis severity in the present study may reflect shared inflammatory pathways across chronic conditions rather than a direct disease-specific effect.^26,27 This interpretation is consistent with the view that these indices capture generalized inflammatory status rather than condition-specific pathophysiological processes.

The main strengths of this study are its relatively large sample size and the simultaneous evaluation of several hematological inflammatory indices, including the less frequently studied SII, SIRI, and PIV. To the best of our knowledge, studies evaluating these composite indices, particularly PIV, in relation to radiographic severity of knee osteoarthritis remain limited.

Importantly, the observed associations may largely reflect underlying systemic metabolic and inflammatory conditions—particularly obesity and metabolic syndrome—rather than osteoarthritis-specific mechanisms. These factors are known to influence both CBC-derived inflammatory indices and disease severity, raising the possibility that the observed relationships are not causal but rather driven by shared upstream determinants. Therefore, residual confounding is likely to be substantial and may account for part or all of the observed associations.

This study has several important limitations. First, its retrospective single-center design limits causal inference and generalizability. Second, key potential confounders such as body mass index, smoking status, metabolic comorbidities, and medication use were not available in the dataset and therefore could not be included in the multivariable analyses. Given that these factors are closely associated with both systemic inflammation and osteoarthritis severity, the observed associations may be partially explained by residual confounding. In particular, obesity and metabolic syndrome are known to contribute to chronic low-grade systemic inflammation, which may influence both CBC-derived inflammatory indices and disease severity. Therefore, these inflammatory markers may reflect underlying systemic metabolic inflammation rather than osteoarthritis-specific pathological processes, which limits the interpretation of these findings as independent disease-related effects. Third, radiographic severity based on the Kellgren–Lawrence classification does not necessarily reflect symptom severity or functional status, as clinical outcome measures such as pain scores or functional indices were not available. Finally, the discriminatory performance of the investigated biomarkers was limited, and the proposed cut-off values were not externally validated.

Overall, our findings support an association between CBC-derived inflammatory indices and radiographic severity in knee osteoarthritis. Further prospective, multicenter studies incorporating clinical severity measures and broader adjustment for confounding variables are needed to clarify the clinical utility of these biomarkers.

Conclusion

In conclusion, CBC-derived inflammatory indices, particularly SIRI, PIV, and SII, were associated with radiographic severity in knee osteoarthritis. However, the overall discriminatory performance of these markers was poor to modest, indicating limited clinical utility for stage differentiation. In addition, the lack of adjustment for important confounding variables limits the interpretation of these associations. Therefore, these indices should be considered adjunctive markers reflecting systemic inflammatory burden rather than disease-specific indicators, and they should not be used as stand-alone tools in clinical decision-making. Further prospective studies with comprehensive clinical data are required to clarify their potential clinical role.

Footnotes

ORCID iD

Hakan Yolaçan

Ethical considerations

This study was approved by the Ethics Committee of Aksaray University Training and Research Hospital in 2025 and with the decision numbered 139.

Consent to participate

There is no information of patient in the submitted manuscript.

Author contribution

Hakan Yolaçan: Writing – review & editing, Writing – original draft, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Hüseyin Erdal: Writing – original draft, Visualization, Resources, Investigation. Serkan Güler: Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Altman

Asch

Bloch

, et al. Development of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association. Arthritis Rheum 1986; 29(8): 1039–1049. https://doi.org/10.1002/art.1780290816

Arden

Nevitt

. Osteoarthritis: epidemiology. Best Pract Res Clin Rheumatol 2006; 20(1): 3–25. https://doi.org/10.1016/j.berh.2005.09.007

Hussain

Neilly

Baliga

, et al. Knee osteoarthritis: a review of management options. Scott Med J 2016; 61(1): 7–16. https://doi.org/10.1177/0036933015619588

Gavrilovski

Dimovska

Spasov

, et al. Alternative treatment of gonarthrosis: proximal fibular osteotomy. Pril (Makedon Akad Nauk Umet Odd Med Nauki) 2024; 45(1): 13–18. https://doi.org/10.2478/prilozi-2024-0002

Nakagawa

Arai

Mazda

, et al. N-acetylcysteine prevents nitric oxide-induced chondrocyte apoptosis and cartilage degeneration in an experimental model of osteoarthritis. J Orthop Res 2010; 28(2): 156–163. https://doi.org/10.1002/jor.20976

Henrotin

Kurz

Aigner

. Oxygen and reactive oxygen species in cartilage degradation: friends or foes? Osteoarthr Cartil 2005; 13(8): 643–654. https://doi.org/10.1016/j.joca.2005.04.002

Brandt

Dieppe

Radin

. Commentary: is it useful to subset “primary” osteoarthritis? A critique based on evidence regarding the etiopathogenesis of osteoarthritis. Semin Arthritis Rheum 2009; 39(2): 81–95. https://doi.org/10.1016/j.semarthrit.2009.06.001

Gezer

Pehlivan

. Systemic immune-inflammation index and other inflammatory parameters in patients receiving biological or targeted synthetic DMARDs for inflammatory rheumatic disease. Marmara Med J 2022; 35(3): 316–322. https://doi.org/10.5472/marumj.1186732

Kurtul

Özer

. Neutrophil-to-lymphocyte ratio in ocular diseases: a systematic review. Int J Ophthalmol 2019; 12(12): 1951–1958. https://doi.org/10.18240/ijo.2019.12.18

10.

Yan

Chai

. Systemic immune‐inflammation index is associated with disease activity in patients with ankylosing spondylitis. J Clin Lab Anal 2021; 35(9): e23964. https://doi.org/10.1002/jcla.23964

11.

Smith

Triantafillou

Parker

, et al. Synovial inflammation in osteoarthritis: pathogenesis and therapeutic targets. Nat Rev Rheumatol 2023; 19(8): 475–488.

12.

Nelson

. Osteoarthritis epidemiology and risk factors update. Curr Opin Rheumatol 2024; 36(2): 85–92.

13.

Courties

Sellam

. Osteoarthritis year in review 2024: epidemiology and therapy. Osteoarthr Cartil 2024; 32(3): 345–356.

14.

Salimi

Khanzadeh

Lucke-Wold

, et al. Role of neutrophil-to-lymphocyte ratio in osteoarthritis: a systematic review and meta-analysis. World J Orthop. 2025; 16(5): 10614512.

15.

Karaman

Ulusoy

Yılmaz

, et al. Relationship between inflammatory markers and radiographic severity in knee osteoarthritis. Arch Rheumatol. 2024; 39(2): 150–158.

16.

Taşoğlu

Bölük

Şahin Onat

, et al.

Is blood neutrophil-lymphocyte ratio an independent predictor of knee osteoarthritis severity?

Clin Rheumatol 2016; 35: 1579–1583. https://doi.org/10.1007/s10067-016-3170-8

17.

Ionitescu

Vermeşan

Haraguş

, et al. Association of neutrophil to lymphocyte ratio with patient reported outcomes in knee osteoarthritis. Appl Sci 2020; 10(22): 8173. https://doi.org/10.3390/app10228173

18.

Shi

Zhao

Ying

, et al. The relationship of platelet to lymphocyte ratio and neutrophil to monocyte ratio to radiographic grades of knee osteoarthritis. Z Rheumatol 2018; 77: 533–537. https://doi.org/10.1007/s00393-017-0348-7

19.

Cheng

Liu

Zhang

, et al. Association between systemic immune-inflammation index and osteoarthritis: a NHANES study. Osteoarthr Cartil 2026; 34(1): 55–63.

20.

Chen

Zhang

, et al. Systemic immune-inflammation index and risk of osteoarthritis. Front Med 2025; 12: 145678.

21.

Wang

, et al. Systemic inflammation response index and osteoarthritis risk. BMC Musculoskelet Disord 2024; 25: 412.

22.

Zhou

Wang

, et al. Systemic immune-inflammation index and mortality risk in osteoarthritis. BMC Public Health 2024; 24: 1987.

23.

Yılmaz

Karaman

Demirci

, et al. Systemic immune-inflammation index and radiographic severity in knee osteoarthritis. Clin Rheumatol 2025; 44(2): 567–575.

24.

Perruccio

Power

Badley

, et al. Osteoarthritis as a multifactorial disease: mechanical, metabolic, and inflammatory factors. Osteoarthr Cartil 2024; 32(6): 789–798.

25.

Welhaven

Yusuf

Haugen

, et al. Biomarkers in osteoarthritis: current status and future perspectives. Nat Rev Rheumatol 2024; 20(5): 301–315.

26.

Tunca

Özkan

Ergin

, et al. Pan-immune-inflammation value as an independent indicator of isolated coronary artery ectasia. Turk Kardiyol Dern Ars 2026; 54: 41–50. https://doi.org/10.5543/tkda.2025.51336

27.

Tunca

Şengül

Özkan

, et al. Hemoglobin–albumin–lymphocyte–platelet (HALP) score as a novel biomarker for predicting coronary slow flow in patients with angina and/or ischemia and nonobstructive coronary arteries. J Clin Med 2026; 15(3): 1302. https://doi.org/10.3390/jcm15031302