Systemic Inflammatory and Metabolic Risk Factors for Early Periprosthetic Osteolysis After Total Knee Arthroplasty: A Predictive Biomarker Study

Systemic Inflammatory and Metabolic Risk Factors for Early Periprosthetic Osteolysis After Total Knee Arthroplasty: A Predictive Biomarker Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Systemic Inflammatory and Metabolic Risk Factors for Early Periprosthetic Osteolysis After Total Knee Arthroplasty: A Predictive Biomarker Study Lifei Wang¹²³, Hefang Xiao¹²³, Jinming Liu¹²³, Haotian He¹²³, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7633068/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 Purpose: This study aimed to identify early risk factors for periprosthetic osteolysis after total knee arthroplasty (TKA) and establish clinically useful predictive biomarkers. Methods : A retrospective analysis was conducted on patients who underwent TKA at our institution between January 1, 2018, and October 31, 2023. Initially, 871 patients were screened, and 375 met the inclusion criteria after applying strict eligibility standards. Patients were categorized into an osteolysis group (n=17) and a non-osteolysis group (n=358). Data on baseline characteristics (age, gender, BMI, diabetes, and hypertension history) and postoperative laboratory results were collected. Logistic regression analyses identified independent risk factors for osteolysis, with subgroup analyses also performed. Results : Multivariate logistic regression analysis identified elevated leukocyte count (OR=1.47, 95%CI: 1.19-1.80, P<0.001), BMI (OR=1.24, 95%CI: 1.03-1.49, P=0.021), and FIB-4 index (OR=2.08, 95%CI: 1.23-3.52, P=0.006) as significant positive risk factors, while the uric acid-to-creatinine ratio (UACR) was negatively associated (OR=0.51, 95%CI: 0.31-0.82, P=0.006). Subgroup analyses revealed variations in these risk factors across gender, age, diabetes, and hypertension status. Conclusion : This study demonstrates that leukocyte count, BMI, FIB-4 index, and UACR are independent risk factors for early periprosthetic osteolysis after TKA. These findings may assist in the early identification and management of high-risk patients, thereby reducing postoperative complications and improving patient outcomes. Total knee arthroplasty osteolysis risk factors biomarkers BMI leukocyte count FIB-4 index UACR Figures Figure 1 1. Introduction Periprosthetic osteolysis remains one of the major causes of failure after total knee arthroplasty (TKA). Severe osteolysis can lead to prosthetic loosening and dislocation(1, 2).This not only directly results in suboptimal surgical outcomes or treatment failure, but also presents significant challenges for revision procedures(3, 4). With the increasing volume of TKA procedures, osteolysis has become one of the main complications affecting long-term prosthesis survival and postoperative function(5).In recent years, as TKA has been increasingly applied to patients with end-stage joint diseases such as knee osteoarthritis, more attention has been given to the management and prevention of postoperative complications. Osteolysis is triggered by local chronic inflammatory responses induced by wear debris—particularly polyethylene particles. The underlying mechanism involves activation of macrophages by wear particles, which then release inflammatory cytokines that stimulate osteoclasts, leading to progressive bone resorption and structural destruction of the periprosthetic bone. Current research indicates that osteolysis is associated with multiple factors, including mechanical issues such as prosthetic malalignment, degree of polyethylene liner wear, and prosthesis design (e.g., use of metal-backed patellar components). Moreover, patient-specific variables also contribute to the risk of osteolysis. For instance, individuals with high body mass index (BMI) may experience greater joint loading and activity-related wear, predisposing them to prosthesis wear and osteolysis(6). Imaging modalities such as plain radiography, CT, and PET-CT are commonly used to evaluate osteolysis. Among them, conventional radiography remains the most commonly used preliminary diagnostic method; however, its sensitivity is low, reportedly only about 26%(6).Special imaging techniques, such as posterior condylar oblique view X-rays, may improve detection rates. In recent years, CT and MRI have significantly improved the accuracy of osteolysis diagnosis, allowing clear visualization of lesion size, location, cortical integrity, and soft tissue involvement. However, osteolytic lesions typically appear on X-ray only at relatively advanced stages, making early identification difficult(7).Although PET-CT is highly sensitive, its high cost limits widespread clinical application. While chronic inflammation and metabolic dysregulation have been implicated in the pathogenesis of osteolysis, there remains a lack of simple and accessible laboratory markers to predict risk. Whether routine postoperative laboratory parameters can be used to predict osteolysis in high-risk TKA patients remains an important unanswered question. This study retrospectively screened patients for early-stage osteolysis using radiographic criteria, and systematically analyzed its correlation with various laboratory parameters. The goal was to identify simple, clinically applicable biomarkers for early prediction of osteolysis risk. Early identification of high-risk individuals and timely intervention are crucial to reducing implant failure and avoiding revision surgery. 2. Materials and Methods 2.1. Study Design and Patients This was a single-center retrospective study approved by the institutional ethics committee. Patients who underwent total knee arthroplasty (TKA) between January 1, 2018, and October 31, 2023, were screened via the hospital's medical record system. A total of 871 cases were initially identified. The diagnostic criteria for periprosthetic osteolysis included the presence of radiolucent lines > 2 mm around the prosthesis on X-ray, or evidence of focal bone defects or disappearance of trabecular bone structure. To ensure data integrity and consistency, strict inclusion and exclusion criteria were applied. Inclusion criteria required that patients had undergone at least 3 months of postoperative follow-up with complete laboratory test results and standard anteroposterior and lateral radiographs of the ipsilateral knee. Only early-stage osteolysis cases (i.e., without visible prosthetic loosening or displacement) were included. Exclusion criteria included the absence of follow-up lab tests or imaging (n = 496) and cases requiring revision surgery. After screening, 375 patients were eligible for inclusion, comprising 17 patients with radiographically confirmed osteolysis and 358 without osteolysis. Baseline demographic and clinical variables were extracted from the records, including age, sex, body mass index (BMI), follow-up interval, history of diabetes (coded as 0 = no diabetes, 1 = diabetes without complications, 2 = diabetes with complications), and history of hypertension (0 = no, 1 = yes). Full baseline characteristics are presented in Table 1. In addition, routine hematological and serological parameters from postoperative follow-up were collected for statistical analysis. 2.2. Statistical Analysis Continuous variables were expressed as mean ± standard deviation (x̄ ± s), while categorical variables were presented as frequencies and percentages (n, %). Comparisons between groups were performed using the Student’s t-test for continuous variables and chi-square test for categorical variables to evaluate baseline comparability. To explore associations with periprosthetic osteolysis, univariate logistic regression analysis was first conducted to screen potential predictors. Variables with P-values < 0.1 in univariate analysis were then entered into a multivariate logistic regression model to identify independent risk factors. A bidirectional stepwise regression approach was used to optimize model performance. This method combines forward selection and backward elimination, aiming to balance model complexity and predictive accuracy. The stepwise procedure was as follows: Initialization: The model was initialized either from an empty model (adding variables sequentially) or from a full model (removing variables one by one). Forward selection: Variables with P-values < 0.1 and the greatest incremental improvement to the model were added. Backward elimination: Variables with P-values ≥ 0.1 and minimal contribution were removed. Iteration continued until all variables in the final model had P-values < 0.1. All statistical analyses were conducted using the Storm Statistics Platform (Zstats, www.zstats.net), R version 4.3.3 (2024-02-29), and IBM SPSS Statistics version .This retrospective study was approved by the Ethics Committee of Lanzhou University Second Hospital (Approval No:2025A-734). As the study involved no patient intervention and only used anonymized data, the requirement for informed consent was waived by the committee. 3. Results A total of 375 patients were included in this study. Among them, 358 patients (95.47%) did not develop radiographic evidence of periprosthetic osteolysis, while 17 patients (4.53%) were diagnosed with early-stage osteolysis. The average age of patients without osteolysis was 62.74 years, while that of those with osteolysis was 68.47 years; the difference was statistically significant (t = –2.45, P = 0.015). In terms of BMI, the non-osteolysis group had a mean of 25.31 kg/m², and the osteolysis group had a mean of 26.48 kg/m²; however, the difference was not statistically significant (t = –1.30, P = 0.193). Regarding diabetes status, in the non-osteolysis group, 88.83% had no history of diabetes, 9.22% had diabetes without complications, and 1.96% had diabetes with complications. In the osteolysis group, 70.59% had no diabetes, 11.76% had uncomplicated diabetes, and 17.65% had diabetes with complications. The distribution difference was statistically significant (P = 0.008).There was no significant difference in hypertension distribution between the two groups (χ² = 0.08, P = 0.778), nor in sex distribution (χ² = 0.00, P = 1.000). Detailed results are shown in Table 1 . 3.1. Univariate Logistic Regression In the univariate analysis, several laboratory and clinical parameters were significantly associated with an increased risk of periprosthetic osteolysis. These included leukocyte count, monocyte count, neutrophil count, SIRI (Systemic Inflammation Response Index), PIV (Pan-Immune-Inflammation Value), age, diabetes status, FIB-4 index, serum creatinine, and direct bilirubin (all P < 0.05). Notably, UACR (uric acid to creatinine ratio) and ALT/AST ratio emerged as protective factors, both demonstrating inverse associations with osteolysis risk. In contrast, BMI, SII, NLR, and several other inflammatory markers showed no statistically significant correlation. Detailed regression results are presented in Table 2 . 3.2. Multivariate Logistic Regression Multivariate logistic regression with stepwise selection identified four independent predictors of early periprosthetic osteolysis: leukocyte count, BMI, UACR, and FIB-4 index. After adjusting for potential confounders, all four variables remained statistically significant (P < 0.1). Increased leukocyte count, BMI, and FIB-4 index were positively associated with higher osteolysis risk, while higher UACR was negatively associated. The odds ratios indicated that: A 1-unit increase in leukocyte count was associated with a 47% higher risk of osteolysis; Each additional 1 kg/m² of BMI increased the risk by 24%; Every unit increase in UACR was associated with a 49% reduction in risk; A 1-unit increase in FIB-4 index more than doubled the osteolysis risk. These findings suggest that systemic inflammation, metabolic burden, and hepatic fibrosis may play important roles in the early development of osteolysis. See Table 3 for the full model outputs. 3.2.1. ROC Curve Analysis The ROC curve analysis of the multivariate logistic regression model demonstrated good discriminative ability, with an AUC of 0.820 (95% CI: 0.702–0.918, P < 0.001). This indicates that the model effectively predicts periprosthetic osteolysis after total knee arthroplasty (TKA). See Fig 1 . 3.2.2. Internal Validation Internal validation using the bootstrap method (1,000 resamples) was performed to further evaluate the stability and generalizability of the model. The bootstrap-corrected AUC was 0.820 (95% CI: 0.750–0.847), confirming the stable and reliable predictive performance of the developed model. 3.3. Subgroup Analyses 3.3.1. Sex-based Subgroup In the Women subgroup , leukocyte count (OR = 1.47), FIB-4 index (OR = 2.08), and BMI (OR = 1.24) were significantly associated with osteolysis (P < 0.05), while UACR showed no significant effect. In the Men subgroup , none of the predictors were statistically significant; however, the effect of UACR approached significance (P ≈ 0.05), suggesting a potential sex-related variation or sample size limitation. 3.3.2. Age-based Subgroup Among patients ≥65 years , leukocyte count, BMI, and UACR were significantly associated with osteolysis risk (P < 0.05), while FIB-4 index was not. In the 50–65 and ≤50 years subgroups, none of the predictors were statistically significant, possibly due to lower osteolysis incidence or limited power. 3.3.3. Diabetes-based Subgroup In the non-diabetic population , leukocyte count, FIB-4 index, and BMI remained significant (P < 0.05), whereas UACR was not. Among diabetic patients , FIB-4 index and UACR remained significant, but leukocyte count and BMI were not, potentially due to reduced sample size or other confounding factors. 3.3.4. Hypertension-based Subgroup In hypertensive patients , leukocyte count and UACR were significant predictors (P < 0.05), while FIB-4 index and BMI were not. In non-hypertensive patients , leukocyte count and FIB-4 index were significantly associated with osteolysis (P < 0.05), whereas BMI and UACR were not. Subgroup analyses based on gender, age, diabetes, and hypertension status are summarized in Supplementary Tables 1–4 4. Discussion This study systematically analyzed the clinical and laboratory parameters of patients after total knee arthroplasty (TKA) and identified leukocyte count, body mass index (BMI), FIB-4 index, and uric acid to creatinine ratio (UACR) as independent predictors of early periprosthetic osteolysis. These variables remained significant even after adjustment for confounders in the multivariate logistic model. Subgroup analyses further indicated that some risk factors may be modulated by sex, age, and comorbidities such as diabetes and hypertension. These findings suggest that osteolysis is a multifactorial complication involving inflammatory, metabolic, and systemic pathways, underscoring the need for individualized monitoring and intervention strategies. Specifically, our results demonstrated that each 1-unit increase in BMI was associated with a 24% increase in the risk of osteolysis. This is consistent with existing literature, which has consistently shown that elevated BMI is linked to a higher incidence of postoperative complications, including osteolysis, aseptic loosening, and implant failure. From a biomechanical perspective, obesity increases joint load, accelerates wear of the polyethylene liner, promotes particle accumulation, and subsequently enhances osteoclast-mediated bone resorption(8, 9). In addition, adipose tissue-derived cytokines such as IL-6 and TNF-α, as well as increased oxidative stress, may further promote bone loss(10). Prior research has estimated that for each 1 kg/m² increase in BMI, the mechanical burden on the implant rises by approximately 3–4 kg(9). A scoping review covering 71 studies found that approximately 42% reported a higher complication rate in obese patients after TKA, further supporting the necessity of weight management in these individual(11). Our study also confirmed that increased leukocyte count is significantly associated with osteolysis risk, suggesting an important role of systemic inflammation in its pathogenesis. Bone metabolism and the hematopoietic system are closely interlinked, and numerous studies have shown that circulating leukocyte levels reflect changes in bone remodeling(12–15). For instance, Valderrábano et al. found that elderly men with rapid hip bone loss (> 0.5% per year) were more likely to exhibit anemia, elevated neutrophil counts, and reduced lymphocyte levels(12). Kristjansdottir et al. reported similar findings in the Swedish MrOS cohort, where neutrophil count was inversely correlated with bone mineral density even after adjusting for BMI(13). Recent single-cell RNA sequencing has shown that osteoporotic patients exhibit shifts in monocyte subsets, with upregulation of inflammation and osteoclast activation pathways, especially CD16⁺ terminal monocytes(15). In addition, studies in female populations have shown that both red and white blood cell counts are positively correlated with bone mineral density and bone microarchitecture parameters, suggesting that optimal bone health depends on a well-functioning hematopoietic and immune microenvironment(13) .These findings suggest that leukocyte count is not only an inflammatory biomarker but also reflects changes in the osteoimmune microenvironment. Thus, it may serve as a practical marker for early detection of periprosthetic osteolysis risk. Uric acid is the final product of purine nucleotide metabolism in the human body(16). Its extracellular antioxidant properties are believed to play a role in skeletal metabolism(17). Regarding the relationship between creatinine and bone mineral density (BMD), creatinine also exhibits antioxidant capacity in vivo, and several studies suggest that creatinine may serve as a protective factor for bone density. Serum uric acid levels have been shown to correlate positively with BMD, indicating that hyperuricemia may help prevent osteoporosis(18).In our study, the uric acid-to-creatinine ratio (UACR) was significantly associated with a reduced risk of periprosthetic osteolysis. Specifically, an increase of 1 unit in UACR was associated with a 49% decrease in osteolysis risk (OR = 0.51), suggesting that UACR may serve as a protective biomarker. Consistent with our findings, a large-scale NHANES-based study by Yu et al. (2025) also demonstrated a significant inverse association between UA/Cr and osteoporosis risk in older adults (OR = 0.83, 95% CI: 0.76–0.91, P < 0.001). Their quartile-based analysis further confirmed that individuals in higher UACR groups had a lower risk of osteoporosis, and the predictive performance of UA/Cr was superior to that of serum uric acid alone(19). These results suggest that UA/Cr may serve as a useful auxiliary index for evaluating bone metabolism risk in the elderly. An elevated FIB-4 index, a liver fibrosis score, was also positively associated with the risk of periprosthetic osteolysis in this study. The FIB-4 index has been widely used to assess the degree of liver fibrosis in patients with chronic liver disease. In recent years, the relationship between liver fibrosis (including non-invasive indicators such as FIB-4) and reduced bone mineral density or increased osteoporosis risk has been the subject of ongoing debate. Several case-control and cross-sectional studies (e.g., by Barchetta and Kim) have found that higher degrees of liver fibrosis are significantly associated with lower bone density and increased osteoporosis risk(20, 21). However, other studies, such as multicenter analyses based on the NHANES database, reported that after adjusting for confounding metabolic factors like BMI and diabetes, the association between liver fibrosis and BMD or osteoporosis was no longer significant(22).These discrepancies may be explained by differences in study populations, disease status (e.g., obesity, diabetes), comorbidities, and assessment methods. A large population-based study from the Bushehr Elderly Health Program found that FIB-4 scores were significantly higher in individuals with osteoporosis, and even after adjustment for confounders, FIB-4 remained negatively correlated with hip and femoral neck BMD as well as trabecular bone score. Moreover, FIB-4 was significantly associated with an increased risk of osteoporosis (OR = 2.12 in women; OR = 1.37 in men)(20).It has been proposed that the link between liver fibrosis and bone metabolism disorders may involve multiple pathological mechanisms, including systemic inflammation, insulin resistance, disruption of the RANKL/OPG system, and interactions involving gut microbiota and the “liver–bone axis”(23–25). Although our study identified FIB-4 as an independent risk factor for periprosthetic osteolysis, existing literature suggests that the impact of liver fibrosis on bone metabolism may vary depending on the population and metabolic context. Future studies should further investigate the mechanistic differences among clinical subgroups and develop targeted prevention and treatment strategies. Subgroup analyses revealed that the magnitude and significance of risk factors varied by sex, age, and comorbidities. For example, leukocyte count and BMI were more strongly associated with osteolysis in females and elderly patients, while FIB-4 index was especially relevant in diabetic patients. These findings support a personalized approach to monitoring and prevention in TKA patients. Declarations 5. Author Contributions Lifei Wang is the first author of this study and made the primary contribution. Lifei Wang: study design, data collection, and initial manuscript drafting; Hefang Xiao: data analysis and figure preparation; Jinming Liu: statistical analysis and methodological guidance; Haotian He, Shicheng Li: case selection and laboratory data review; Bin Geng: project coordination and ethics approval management; Yayi Xia (corresponding author): proposed the study concept, supervised the overall project, guided manuscript writing, and finalized the revisions. All authors have read and approved the final version of the manuscript. 6. Human Ethics and Consent to Participate declarations: This study strictly follows the ethical guidelines of the Declaration of Helsinki. The research protocol was reviewed and approved by the Ethics Committee of The Second Hospital of Lanzhou University (Approval Number: 2025A - 734). Prior to the commencement of the study, we provided detailed information about the research to all potential participants, including the study's purpose, methods, possible risks, and benefits. Participants who fully understood and were willing to participate signed written informed consent forms, granting permission for us to use their relevant data. 7. Funding The National Natural Science Foundation of China (82060405 and 82360436); Lanzhou Science and Technology Plan Program (2021-RC-102); Natural Science Foundation of Gansu Province (22JR5RA943、22JR5RA956, 24YFFA043, and 23JRRA1500). 8. Conflict of Interest: The authors declare that they have no conflicts of interest to disclose. References Chen Y, Zhou L, Guan M, Jin S, Tan P, Fu X, et al. Multifunctionally disordered TiO(2) nanoneedles prevent periprosthetic infection and enhance osteointegration by killing bacteria and modulating the osteoimmune microenvironment. Theranostics. 2024;14(15):6016-35. Ripollés-Melchor J, Abad-Motos A, Díez-Remesal Y, Aseguinolaza-Pagola M, Padin-Barreiro L, Sánchez-Martín R, et al. Association Between Use of Enhanced Recovery After Surgery Protocol and Postoperative Complications in Total Hip and Knee Arthroplasty in the Postoperative Outcomes Within Enhanced Recovery After Surgery Protocol in Elective Total Hip and Knee Arthroplasty Study (POWER2). JAMA Surg. 2020;155(4):e196024. Zaballa E, Dennison E, Walker-Bone K. Function and employment after total hip replacement in older adults: A narrative review. Maturitas. 2023;167:8-16. Hussain SM, Ackerman IN, Wang Y, English DR, Wluka AE, Giles GG, et al. Trajectories of body mass index from early adulthood to late midlife and incidence of total knee arthroplasty for osteoarthritis: findings from a prospective cohort study. Osteoarthritis Cartilage. 2023;31(3):397-405. Hu CY, Yoon TR. Recent updates for biomaterials used in total hip arthroplasty. Biomater Res. 2018;22:33. Dalling JG, Math K, Scuderi GR. Evaluating the progression of osteolysis after total knee arthroplasty. J Am Acad Orthop Surg. 2015;23(3):173-80. Walker EA, Fox MG, Blankenbaker DG, French CN, Frick MA, Hanna TN, et al. ACR Appropriateness Criteria® Imaging After Total Knee Arthroplasty: 2023 Update. J Am Coll Radiol. 2023;20(11s):S433-s54. Uvodich ME, Dugdale EM, Pagnano MW, Berry DJ, Abdel MP, Bedard NA. Outcomes of Obese Patients Undergoing Primary Total Knee Arthroplasty: Trends Over 30 Years. J Bone Joint Surg Am. 2024;106(21):1963-70. Sodhi N, Anis HK, Vakharia RM, Acuña AJ, Gold PA, Garbarino LJ, et al. What Are Risk Factors for Infection after Primary or Revision Total Joint Arthroplasty in Patients Older Than 80 Years? Clin Orthop Relat Res. 2020;478(8):1741-51. Chait A, den Hartigh LJ. Adipose Tissue Distribution, Inflammation and Its Metabolic Consequences, Including Diabetes and Cardiovascular Disease. Front Cardiovasc Med. 2020;7:22. Jester R, Rodney A. The relationship between obesity and primary Total Knee Replacement: A scoping review of the literature. Int J Orthop Trauma Nurs. 2021;42:100850. Valderrábano RJ, Lui LY, Lee J, Cummings SR, Orwoll ES, Hoffman AR, et al. Bone Density Loss Is Associated With Blood Cell Counts. J Bone Miner Res. 2017;32(2):212-20. Kristjansdottir HL, Mellström D, Johansson P, Karlsson M, Vandenput L, Lorentzon M, et al. High platelet count is associated with low bone mineral density: The MrOS Sweden cohort. Osteoporos Int. 2021;32(5):865-71. Polineni S, Resulaj M, Faje AT, Meenaghan E, Bredella MA, Bouxsein M, et al. Red and White Blood Cell Counts Are Associated With Bone Marrow Adipose Tissue, Bone Mineral Density, and Bone Microarchitecture in Premenopausal Women. J Bone Miner Res. 2020;35(6):1031-9. Tao L, Jiang W, Li H, Wang X, Tian Z, Yang K, et al. Single-cell RNA sequencing reveals that an imbalance in monocyte subsets rather than changes in gene expression patterns is a feature of postmenopausal osteoporosis. J Bone Miner Res. 2024;39(7):980-93. Dalbeth N, Gosling AL, Gaffo A, Abhishek A. Gout. Lancet. 2021;397(10287):1843-55. Lin KM, Lu CL, Hung KC, Wu PC, Pan CF, Wu CJ, et al. The Paradoxical Role of Uric Acid in Osteoporosis. Nutrients. 2019;11(9). Chen L, Peng Y, Fang F, Chen J, Pan L, You L. Correlation of serum uric acid with bone mineral density and fragility fracture in patients with primary osteoporosis: a single-center retrospective study of 253 cases. Int J Clin Exp Med. 2015;8(4):6291-4. Yu J, Xu C, Ma D, Li Y, Yang L. Serum uric acid/creatinine ratio and osteoporosis in the elderly: a NHANES study. Front Med (Lausanne). 2025;12:1530116. Barchetta I, Lubrano C, Cimini FA, Dule S, Passarella G, Dellanno A, et al. Liver fibrosis is associated with impaired bone mineralization and microstructure in obese individuals with non-alcoholic fatty liver disease. Hepatol Int. 2023;17(2):357-66. Kim G, Kim KJ, Rhee Y, Lim SK. Significant liver fibrosis assessed using liver transient elastography is independently associated with low bone mineral density in patients with non-alcoholic fatty liver disease. PLoS One. 2017;12(7):e0182202. Li H, Luo H, Zhang Y, Liu L, Lin R. Association of Metabolic Dysfunction-Associated Fatty Liver Disease and Liver Stiffness With Bone Mineral Density in American Adults. Front Endocrinol (Lausanne). 2022;13:891382. Nakchbandi IA. Osteoporosis and fractures in liver disease: relevance, pathogenesis and therapeutic implications. World J Gastroenterol. 2014;20(28):9427-38. Guss JD, Taylor E, Rouse Z, Roubert S, Higgins CH, Thomas CJ, et al. The microbial metagenome and bone tissue composition in mice with microbiome-induced reductions in bone strength. Bone. 2019;127:146-54. Yan J, Charles JF. Gut Microbiome and Bone: to Build, Destroy, or Both? Curr Osteoporos Rep. 2017;15(4):376-84. Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Table3.docx SupplementaryMaterials.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7633068","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534447587,"identity":"c85a7fb8-3642-4412-a2ff-b7040223d599","order_by":0,"name":"Lifei Wang¹²³","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lifei","middleName":"","lastName":"Wang¹²³","suffix":""},{"id":534447588,"identity":"6191450e-c6e7-4bc8-a0ea-7a798dc9f348","order_by":1,"name":"Hefang Xiao¹²³","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hefang","middleName":"","lastName":"Xiao¹²³","suffix":""},{"id":534447589,"identity":"4d460027-ab0c-4c81-8dff-1941063a70d1","order_by":2,"name":"Jinming Liu¹²³","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinming","middleName":"","lastName":"Liu¹²³","suffix":""},{"id":534447590,"identity":"68227585-e9ad-4a5c-853b-508364754068","order_by":3,"name":"Haotian He¹²³","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haotian","middleName":"","lastName":"He¹²³","suffix":""},{"id":534447591,"identity":"de1c680a-5a5a-4c7d-a543-94d02c9d63e5","order_by":4,"name":"Shicheng Li¹²³，","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shicheng","middleName":"","lastName":"Li¹²³，","suffix":""},{"id":534447592,"identity":"3d78e378-edbe-4089-a3a0-058140d91e63","order_by":5,"name":"Bin Geng¹²³","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Geng¹²³","suffix":""},{"id":534447593,"identity":"fe63204e-c694-4698-b84f-db82ace6ca66","order_by":6,"name":"Yayi Xia¹²³","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDACZhBRYQFmS5Cg5YwEKVpAgLGNFC0Gx5mfSfPOk7A3OMB88DYPg10eQS2SzWxm0rzbJJgNDrAlW/MwJBcT1MLPzADWwmZwgMdMmofhQGIDIS1szOzfpHnnSPAYHOD/RpwWfmag4bwNEhJAW9iI0yLZzFNsOeeYhIHkYTZjyzkGyYS1GJw/vvHGmxobe77jzQ9vvKmwI6wFCFikeEAUOE4NiFAPUvvxB3EKR8EoGAWjYKQCABPKLlf+vm3YAAAAAElFTkSuQmCC","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yayi","middleName":"","lastName":"Xia¹²³","suffix":""}],"badges":[],"createdAt":"2025-09-16 17:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7633068/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7633068/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94988437,"identity":"0d568c50-8961-4813-aa68-f55876867f87","added_by":"auto","created_at":"2025-11-03 07:09:02","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17805040,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/410f76ae5e163c2e836165e2.tif"},{"id":94988187,"identity":"8ef02f70-e3b0-45da-9622-9eeb81c082da","added_by":"auto","created_at":"2025-11-03 07:06:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":282735,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/d7e6872793742e51eb0bb956.docx"},{"id":94968537,"identity":"2782160f-d196-4b4f-81ac-dfb687be994d","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16064,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/e66b1ce92da8a6f72c6e2e1c.docx"},{"id":94988351,"identity":"3950ca65-64de-4704-98b5-43e07ad2ae34","added_by":"auto","created_at":"2025-11-03 07:08:45","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23606,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/2e8be5d5a89879ddd7226843.docx"},{"id":94968546,"identity":"ad01ce37-3fb3-4765-ab07-a1d36bc8e7b5","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17547,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/0225c203770a4716c71a51a8.docx"},{"id":94989003,"identity":"ebe02830-06c5-4de0-89ff-508581a2a70a","added_by":"auto","created_at":"2025-11-03 07:11:40","extension":"json","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8418,"visible":true,"origin":"","legend":"","description":"","filename":"e589dc1e4f024f319db6c6a37f31617b.json","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/3370508cf7d189d5dc1dcab2.json"},{"id":94968539,"identity":"09843320-cee1-4f2e-a439-faf202939370","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16156,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/764fd6bf30be7830b657f86e.docx"},{"id":94968553,"identity":"4bcf3195-314f-44b0-a2c8-ddd33ee2861a","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121249,"visible":true,"origin":"","legend":"","description":"","filename":"e589dc1e4f024f319db6c6a37f31617b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/5d7709eb2a3f7a40c4cbaf2e.xml"},{"id":94968551,"identity":"67782ac4-485a-4f2e-adf0-ea2a1e8a42ea","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17805040,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/59a71493ccaa0c8fff5d933a.tif"},{"id":94968548,"identity":"9247835a-54da-4c44-b814-d64ab2f29a5e","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4507722,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/df0802a22e05d67f4e21b757.jpeg"},{"id":94968545,"identity":"1d8ddcb2-a6de-4d1b-afed-1408c646b815","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33468,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/3cfa305afd43b078ce9dc5a1.png"},{"id":94988734,"identity":"cd68bad9-2019-45e2-a979-242e454a2f3f","added_by":"auto","created_at":"2025-11-03 07:10:38","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122223,"visible":true,"origin":"","legend":"","description":"","filename":"e589dc1e4f024f319db6c6a37f31617b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/4c4b12ad6664cece374e0d14.xml"},{"id":94968549,"identity":"92bd6c01-8d54-4ca1-8745-429f915d890f","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129098,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/c7a5bdee45108911fa82c85e.html"},{"id":94968541,"identity":"40a9571f-d23b-40ef-b725-5570fa11b9ed","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":419754,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/d7dbb5e0da9d97d4856281b8.png"},{"id":95181079,"identity":"2cbea102-6d23-443b-9dac-92a7c95d2a85","added_by":"auto","created_at":"2025-11-05 08:23:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":720498,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/78e2b04d-fc59-41a9-acfb-acc008019370.pdf"},{"id":94968535,"identity":"73e682e1-3ed5-45bb-9cc8-cdba7317bd51","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16064,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/0260b2d13896b7c52b648182.docx"},{"id":94968536,"identity":"15d95e1a-d32d-4b21-9ef5-f3e5109973ed","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23606,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/2dce80de49ef2ad99a248194.docx"},{"id":94968543,"identity":"b33f52ae-adf6-4f04-9d99-92ec69e2aa8c","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17547,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/5f137fcdd60d23a887b5773c.docx"},{"id":94968550,"identity":"fbb55397-581f-4cd7-ba31-d9fbb7ebce67","added_by":"auto","created_at":"2025-11-02 23:31:45","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16156,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633068/v1/ac04d4bbee788b17925612ea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systemic Inflammatory and Metabolic Risk Factors for Early Periprosthetic Osteolysis After Total Knee Arthroplasty: A Predictive Biomarker Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePeriprosthetic osteolysis remains one of the major causes of failure after total knee arthroplasty (TKA). Severe osteolysis can lead to prosthetic loosening and dislocation(1, 2).This not only directly results in suboptimal surgical outcomes or treatment failure, but also presents significant challenges for revision procedures(3, 4). With the increasing volume of TKA procedures, osteolysis has become one of the main complications affecting long-term prosthesis survival and postoperative function(5).In recent years, as TKA has been increasingly applied to patients with end-stage joint diseases such as knee osteoarthritis, more attention has been given to the management and prevention of postoperative complications.\u003c/p\u003e\u003cp\u003eOsteolysis is triggered by local chronic inflammatory responses induced by wear debris\u0026mdash;particularly polyethylene particles. The underlying mechanism involves activation of macrophages by wear particles, which then release inflammatory cytokines that stimulate osteoclasts, leading to progressive bone resorption and structural destruction of the periprosthetic bone. Current research indicates that osteolysis is associated with multiple factors, including mechanical issues such as prosthetic malalignment, degree of polyethylene liner wear, and prosthesis design (e.g., use of metal-backed patellar components). Moreover, patient-specific variables also contribute to the risk of osteolysis. For instance, individuals with high body mass index (BMI) may experience greater joint loading and activity-related wear, predisposing them to prosthesis wear and osteolysis(6).\u003c/p\u003e\u003cp\u003eImaging modalities such as plain radiography, CT, and PET-CT are commonly used to evaluate osteolysis. Among them, conventional radiography remains the most commonly used preliminary diagnostic method; however, its sensitivity is low, reportedly only about 26%(6).Special imaging techniques, such as posterior condylar oblique view X-rays, may improve detection rates. In recent years, CT and MRI have significantly improved the accuracy of osteolysis diagnosis, allowing clear visualization of lesion size, location, cortical integrity, and soft tissue involvement.\u003c/p\u003e\u003cp\u003eHowever, osteolytic lesions typically appear on X-ray only at relatively advanced stages, making early identification difficult(7).Although PET-CT is highly sensitive, its high cost limits widespread clinical application.\u003c/p\u003e\u003cp\u003eWhile chronic inflammation and metabolic dysregulation have been implicated in the pathogenesis of osteolysis, there remains a lack of simple and accessible laboratory markers to predict risk. Whether routine postoperative laboratory parameters can be used to predict osteolysis in high-risk TKA patients remains an important unanswered question. This study retrospectively screened patients for early-stage osteolysis using radiographic criteria, and systematically analyzed its correlation with various laboratory parameters. The goal was to identify simple, clinically applicable biomarkers for early prediction of osteolysis risk. Early identification of high-risk individuals and timely intervention are crucial to reducing implant failure and avoiding revision surgery.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Study Design and Patients\u003c/h2\u003e\n \u003cp\u003eThis was a single-center retrospective study approved by the institutional ethics committee. Patients who underwent total knee arthroplasty (TKA) between January 1, 2018, and October 31, 2023, were screened via the hospital's medical record system. A total of 871 cases were initially identified. The diagnostic criteria for periprosthetic osteolysis included the presence of radiolucent lines \u0026gt; 2 mm around the prosthesis on X-ray, or evidence of focal bone defects or disappearance of trabecular bone structure.\u003c/p\u003e\n \u003cp\u003eTo ensure data integrity and consistency, strict inclusion and exclusion criteria were applied. Inclusion criteria required that patients had undergone at least 3 months of postoperative follow-up with complete laboratory test results and standard anteroposterior and lateral radiographs of the ipsilateral knee. Only early-stage osteolysis cases (i.e., without visible prosthetic loosening or displacement) were included. Exclusion criteria included the absence of follow-up lab tests or imaging (n = 496) and cases requiring revision surgery. After screening, 375 patients were eligible for inclusion, comprising 17 patients with radiographically confirmed osteolysis and 358 without osteolysis.\u003c/p\u003e\n \u003cp\u003eBaseline demographic and clinical variables were extracted from the records, including age, sex, body mass index (BMI), follow-up interval, history of diabetes (coded as 0 = no diabetes, 1 = diabetes without complications, 2 = diabetes with complications), and history of hypertension (0 = no, 1 = yes). Full baseline characteristics are presented in Table 1. In addition, routine hematological and serological parameters from postoperative follow-up were collected for statistical analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eContinuous variables were expressed as mean ± standard deviation (x̄ ± s), while categorical variables were presented as frequencies and percentages (n, %). Comparisons between groups were performed using the Student’s t-test for continuous variables and chi-square test for categorical variables to evaluate baseline comparability.\u003c/p\u003e\n \u003cp\u003eTo explore associations with periprosthetic osteolysis, univariate logistic regression analysis was first conducted to screen potential predictors. Variables with P-values \u0026lt; 0.1 in univariate analysis were then entered into a multivariate logistic regression model to identify independent risk factors. A bidirectional stepwise regression approach was used to optimize model performance. This method combines forward selection and backward elimination, aiming to balance model complexity and predictive accuracy. The stepwise procedure was as follows: Initialization: The model was initialized either from an empty model (adding variables sequentially) or from a full model (removing variables one by one). Forward selection: Variables with P-values \u0026lt; 0.1 and the greatest incremental improvement to the model were added. Backward elimination: Variables with P-values ≥ 0.1 and minimal contribution were removed. Iteration continued until all variables in the final model had P-values \u0026lt; 0.1. All statistical analyses were conducted using the Storm Statistics Platform (Zstats, www.zstats.net), R version 4.3.3 (2024-02-29), and IBM SPSS Statistics version .This retrospective study was approved by the Ethics Committee of Lanzhou University Second Hospital (Approval No:2025A-734). As the study involved no patient intervention and only used anonymized data, the requirement for informed consent was waived by the committee.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 375 patients were included in this study. Among them, 358 patients (95.47%) did not develop radiographic evidence of periprosthetic osteolysis, while 17 patients (4.53%) were diagnosed with early-stage osteolysis. The average age of patients without osteolysis was 62.74 years, while that of those with osteolysis was 68.47 years; the difference was statistically significant (t = \u0026ndash;2.45, P = 0.015).\u003c/p\u003e\n\u003cp\u003eIn terms of BMI, the non-osteolysis group had a mean of 25.31 kg/m\u0026sup2;, and the osteolysis group had a mean of 26.48 kg/m\u0026sup2;; however, the difference was not statistically significant (t = \u0026ndash;1.30, P = 0.193).\u003c/p\u003e\n\u003cp\u003eRegarding diabetes status, in the non-osteolysis group, 88.83% had no history of diabetes, 9.22% had diabetes without complications, and 1.96% had diabetes with complications. In the osteolysis group, 70.59% had no diabetes, 11.76% had uncomplicated diabetes, and 17.65% had diabetes with complications. The distribution difference was statistically significant (P = 0.008).There was no significant difference in hypertension distribution between the two groups (\u0026chi;\u0026sup2; = 0.08, P = 0.778), nor in sex distribution (\u0026chi;\u0026sup2; = 0.00, P = 1.000). Detailed results are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1. Univariate Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the univariate analysis, several laboratory and clinical parameters were significantly associated with an increased risk of periprosthetic osteolysis. These included leukocyte count, monocyte count, neutrophil count, SIRI (Systemic Inflammation Response Index), PIV (Pan-Immune-Inflammation Value), age, diabetes status, FIB-4 index, serum creatinine, and direct bilirubin (all P \u0026lt; 0.05). Notably, UACR (uric acid to creatinine ratio) and ALT/AST ratio emerged as protective factors, both demonstrating inverse associations with osteolysis risk. In contrast, BMI, SII, NLR, and several other inflammatory markers showed no statistically significant correlation. Detailed regression results are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Multivariate Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression with stepwise selection identified four independent predictors of early periprosthetic osteolysis: leukocyte count, BMI, UACR, and FIB-4 index. After adjusting for potential confounders, all four variables remained statistically significant (P \u0026lt; 0.1). Increased leukocyte count, BMI, and FIB-4 index were positively associated with higher osteolysis risk, while higher UACR was negatively associated.\u003c/p\u003e\n\u003cp\u003eThe odds ratios indicated that:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eA 1-unit increase in leukocyte count was associated with a 47% higher risk of osteolysis;\u003c/li\u003e\n \u003cli\u003eEach additional 1 kg/m\u0026sup2; of BMI increased the risk by 24%;\u003c/li\u003e\n \u003cli\u003eEvery unit increase in UACR was associated with a 49% reduction in risk;\u003c/li\u003e\n \u003cli\u003eA 1-unit increase in FIB-4 index more than doubled the osteolysis risk.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese findings suggest that systemic inflammation, metabolic burden, and hepatic fibrosis may play important roles in the early development of osteolysis. See \u003cstrong\u003eTable 3\u003c/strong\u003e for the full model outputs.\u003c/p\u003e\n\u003cp\u003e3.2.1. \u003cstrong\u003eROC Curve Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ROC curve analysis of the multivariate logistic regression model demonstrated good discriminative ability, with an AUC of 0.820 (95% CI: 0.702\u0026ndash;0.918, P \u0026lt; 0.001). This indicates that the model effectively predicts periprosthetic osteolysis after total knee arthroplasty (TKA). See \u003cstrong\u003eFig 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e3.2.2. \u003cstrong\u003eInternal Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInternal validation using the bootstrap method (1,000 resamples) was performed to further evaluate the stability and generalizability of the model. The bootstrap-corrected AUC was 0.820 (95% CI: 0.750\u0026ndash;0.847), confirming the stable and reliable predictive performance of the developed model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSubgroup Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSex-based Subgroup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the Women \u003cstrong\u003esubgroup\u003c/strong\u003e, leukocyte count (OR = 1.47), FIB-4 index (OR = 2.08), and BMI (OR = 1.24) were significantly associated with osteolysis (P \u0026lt; 0.05), while UACR showed no significant effect. In the Men\u003cstrong\u003e\u0026nbsp;subgroup\u003c/strong\u003e, none of the predictors were statistically significant; however, the effect of UACR approached significance (P \u0026asymp; 0.05), suggesting a potential sex-related variation or sample size limitation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAge-based Subgroup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong patients \u003cstrong\u003e\u0026ge;65 years\u003c/strong\u003e, leukocyte count, BMI, and UACR were significantly associated with osteolysis risk (P \u0026lt; 0.05), while FIB-4 index was not. In the \u003cstrong\u003e50\u0026ndash;65\u003c/strong\u003e and \u003cstrong\u003e\u0026le;50 years\u003c/strong\u003e subgroups, none of the predictors were statistically significant, possibly due to lower osteolysis incidence or limited power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDiabetes-based Subgroup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the \u003cstrong\u003enon-diabetic population\u003c/strong\u003e, leukocyte count, FIB-4 index, and BMI remained significant (P \u0026lt; 0.05), whereas UACR was not. Among \u003cstrong\u003ediabetic patients\u003c/strong\u003e, FIB-4 index and UACR remained significant, but leukocyte count and BMI were not, potentially due to reduced sample size or other confounding factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHypertension-based Subgroup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn \u003cstrong\u003ehypertensive patients\u003c/strong\u003e, leukocyte count and UACR were significant predictors (P \u0026lt; 0.05), while FIB-4 index and BMI were not. In \u003cstrong\u003enon-hypertensive patients\u003c/strong\u003e, leukocyte count and FIB-4 index were significantly associated with osteolysis (P \u0026lt; 0.05), whereas BMI and UACR were not. Subgroup analyses based on gender, age, diabetes, and hypertension status are summarized in \u003cstrong\u003eSupplementary Tables 1\u0026ndash;4\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study systematically analyzed the clinical and laboratory parameters of patients after total knee arthroplasty (TKA) and identified leukocyte count, body mass index (BMI), FIB-4 index, and uric acid to creatinine ratio (UACR) as independent predictors of early periprosthetic osteolysis. These variables remained significant even after adjustment for confounders in the multivariate logistic model. Subgroup analyses further indicated that some risk factors may be modulated by sex, age, and comorbidities such as diabetes and hypertension. These findings suggest that osteolysis is a multifactorial complication involving inflammatory, metabolic, and systemic pathways, underscoring the need for individualized monitoring and intervention strategies.\u003c/p\u003e\u003cp\u003eSpecifically, our results demonstrated that each 1-unit increase in BMI was associated with a 24% increase in the risk of osteolysis. This is consistent with existing literature, which has consistently shown that elevated BMI is linked to a higher incidence of postoperative complications, including osteolysis, aseptic loosening, and implant failure. From a biomechanical perspective, obesity increases joint load, accelerates wear of the polyethylene liner, promotes particle accumulation, and subsequently enhances osteoclast-mediated bone resorption(8, 9). In addition, adipose tissue-derived cytokines such as IL-6 and TNF-α, as well as increased oxidative stress, may further promote bone loss(10). Prior research has estimated that for each 1 kg/m\u0026sup2; increase in BMI, the mechanical burden on the implant rises by approximately 3\u0026ndash;4 kg(9). A scoping review covering 71 studies found that approximately 42% reported a higher complication rate in obese patients after TKA, further supporting the necessity of weight management in these individual(11).\u003c/p\u003e\u003cp\u003eOur study also confirmed that increased leukocyte count is significantly associated with osteolysis risk, suggesting an important role of systemic inflammation in its pathogenesis. Bone metabolism and the hematopoietic system are closely interlinked, and numerous studies have shown that circulating leukocyte levels reflect changes in bone remodeling(12\u0026ndash;15). For instance, Valderr\u0026aacute;bano et al. found that elderly men with rapid hip bone loss (\u0026gt;\u0026thinsp;0.5% per year) were more likely to exhibit anemia, elevated neutrophil counts, and reduced lymphocyte levels(12). Kristjansdottir et al. reported similar findings in the Swedish MrOS cohort, where neutrophil count was inversely correlated with bone mineral density even after adjusting for BMI(13). Recent single-cell RNA sequencing has shown that osteoporotic patients exhibit shifts in monocyte subsets, with upregulation of inflammation and osteoclast activation pathways, especially CD16⁺ terminal monocytes(15). In addition, studies in female populations have shown that both red and white blood cell counts are positively correlated with bone mineral density and bone microarchitecture parameters, suggesting that optimal bone health depends on a well-functioning hematopoietic and immune microenvironment(13) .These findings suggest that leukocyte count is not only an inflammatory biomarker but also reflects changes in the osteoimmune microenvironment. Thus, it may serve as a practical marker for early detection of periprosthetic osteolysis risk.\u003c/p\u003e\u003cp\u003eUric acid is the final product of purine nucleotide metabolism in the human body(16). Its extracellular antioxidant properties are believed to play a role in skeletal metabolism(17). Regarding the relationship between creatinine and bone mineral density (BMD), creatinine also exhibits antioxidant capacity in vivo, and several studies suggest that creatinine may serve as a protective factor for bone density. Serum uric acid levels have been shown to correlate positively with BMD, indicating that hyperuricemia may help prevent osteoporosis(18).In our study, the uric acid-to-creatinine ratio (UACR) was significantly associated with a reduced risk of periprosthetic osteolysis. Specifically, an increase of 1 unit in UACR was associated with a 49% decrease in osteolysis risk (OR\u0026thinsp;=\u0026thinsp;0.51), suggesting that UACR may serve as a protective biomarker. Consistent with our findings, a large-scale NHANES-based study by Yu et al. (2025) also demonstrated a significant inverse association between UA/Cr and osteoporosis risk in older adults (OR\u0026thinsp;=\u0026thinsp;0.83, 95% CI: 0.76\u0026ndash;0.91, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Their quartile-based analysis further confirmed that individuals in higher UACR groups had a lower risk of osteoporosis, and the predictive performance of UA/Cr was superior to that of serum uric acid alone(19). These results suggest that UA/Cr may serve as a useful auxiliary index for evaluating bone metabolism risk in the elderly.\u003c/p\u003e\u003cp\u003eAn elevated FIB-4 index, a liver fibrosis score, was also positively associated with the risk of periprosthetic osteolysis in this study. The FIB-4 index has been widely used to assess the degree of liver fibrosis in patients with chronic liver disease. In recent years, the relationship between liver fibrosis (including non-invasive indicators such as FIB-4) and reduced bone mineral density or increased osteoporosis risk has been the subject of ongoing debate. Several case-control and cross-sectional studies (e.g., by Barchetta and Kim) have found that higher degrees of liver fibrosis are significantly associated with lower bone density and increased osteoporosis risk(20, 21). However, other studies, such as multicenter analyses based on the NHANES database, reported that after adjusting for confounding metabolic factors like BMI and diabetes, the association between liver fibrosis and BMD or osteoporosis was no longer significant(22).These discrepancies may be explained by differences in study populations, disease status (e.g., obesity, diabetes), comorbidities, and assessment methods. A large population-based study from the Bushehr Elderly Health Program found that FIB-4 scores were significantly higher in individuals with osteoporosis, and even after adjustment for confounders, FIB-4 remained negatively correlated with hip and femoral neck BMD as well as trabecular bone score. Moreover, FIB-4 was significantly associated with an increased risk of osteoporosis (OR\u0026thinsp;=\u0026thinsp;2.12 in women; OR\u0026thinsp;=\u0026thinsp;1.37 in men)(20).It has been proposed that the link between liver fibrosis and bone metabolism disorders may involve multiple pathological mechanisms, including systemic inflammation, insulin resistance, disruption of the RANKL/OPG system, and interactions involving gut microbiota and the \u0026ldquo;liver\u0026ndash;bone axis\u0026rdquo;(23\u0026ndash;25). Although our study identified FIB-4 as an independent risk factor for periprosthetic osteolysis, existing literature suggests that the impact of liver fibrosis on bone metabolism may vary depending on the population and metabolic context. Future studies should further investigate the mechanistic differences among clinical subgroups and develop targeted prevention and treatment strategies.\u003c/p\u003e\u003cp\u003eSubgroup analyses revealed that the magnitude and significance of risk factors varied by sex, age, and comorbidities. For example, leukocyte count and BMI were more strongly associated with osteolysis in females and elderly patients, while FIB-4 index was especially relevant in diabetic patients. These findings support a personalized approach to monitoring and prevention in TKA patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLifei Wang is the first author of this study and made the primary contribution. Lifei Wang: study design, data collection, and initial manuscript drafting; Hefang Xiao: data analysis and figure preparation; Jinming Liu: statistical analysis and methodological guidance; Haotian He, Shicheng Li: case selection and laboratory data review; Bin Geng: project coordination and ethics approval management; Yayi Xia (corresponding author): proposed the study concept, supervised the overall project, guided manuscript writing, and finalized the revisions. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp; Human Ethics and Consent to Participate declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study strictly follows the ethical guidelines of the Declaration of Helsinki. The research protocol was reviewed and approved by the Ethics Committee of The Second Hospital of Lanzhou University (Approval Number: 2025A - 734). Prior to the commencement of the study, we provided detailed information about the research to all potential participants, including the study\u0026apos;s purpose, methods, possible risks, and benefits. Participants who fully understood and were willing to participate signed written informed consent forms, granting permission for us to use their relevant data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.\u0026nbsp; Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Natural Science Foundation of China (82060405 and 82360436); Lanzhou Science and Technology Plan Program (2021-RC-102); Natural Science Foundation of Gansu Province (22JR5RA943、22JR5RA956, 24YFFA043, and 23JRRA1500).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.\u0026nbsp; Conflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen Y, Zhou L, Guan M, Jin S, Tan P, Fu X, et al. Multifunctionally disordered TiO(2) nanoneedles prevent periprosthetic infection and enhance osteointegration by killing bacteria and modulating the osteoimmune microenvironment. Theranostics. 2024;14(15):6016-35.\u003c/li\u003e\n\u003cli\u003eRipoll\u0026eacute;s-Melchor J, Abad-Motos A, D\u0026iacute;ez-Remesal Y, Aseguinolaza-Pagola M, Padin-Barreiro L, S\u0026aacute;nchez-Mart\u0026iacute;n R, et al. Association Between Use of Enhanced Recovery After Surgery Protocol and Postoperative Complications in Total Hip and Knee Arthroplasty in the Postoperative Outcomes Within Enhanced Recovery After Surgery Protocol in Elective Total Hip and Knee Arthroplasty Study (POWER2). JAMA Surg. 2020;155(4):e196024.\u003c/li\u003e\n\u003cli\u003eZaballa E, Dennison E, Walker-Bone K. Function and employment after total hip replacement in older adults: A narrative review. Maturitas. 2023;167:8-16.\u003c/li\u003e\n\u003cli\u003eHussain SM, Ackerman IN, Wang Y, English DR, Wluka AE, Giles GG, et al. Trajectories of body mass index from early adulthood to late midlife and incidence of total knee arthroplasty for osteoarthritis: findings from a prospective cohort study. Osteoarthritis Cartilage. 2023;31(3):397-405.\u003c/li\u003e\n\u003cli\u003eHu CY, Yoon TR. Recent updates for biomaterials used in total hip arthroplasty. Biomater Res. 2018;22:33.\u003c/li\u003e\n\u003cli\u003eDalling JG, Math K, Scuderi GR. Evaluating the progression of osteolysis after total knee arthroplasty. J Am Acad Orthop Surg. 2015;23(3):173-80.\u003c/li\u003e\n\u003cli\u003eWalker EA, Fox MG, Blankenbaker DG, French CN, Frick MA, Hanna TN, et al. ACR Appropriateness Criteria\u0026reg; Imaging After Total Knee Arthroplasty: 2023 Update. J Am Coll Radiol. 2023;20(11s):S433-s54.\u003c/li\u003e\n\u003cli\u003eUvodich ME, Dugdale EM, Pagnano MW, Berry DJ, Abdel MP, Bedard NA. Outcomes of Obese Patients Undergoing Primary Total Knee Arthroplasty: Trends Over 30 Years. J Bone Joint Surg Am. 2024;106(21):1963-70.\u003c/li\u003e\n\u003cli\u003eSodhi N, Anis HK, Vakharia RM, Acu\u0026ntilde;a AJ, Gold PA, Garbarino LJ, et al. What Are Risk Factors for Infection after Primary or Revision Total Joint Arthroplasty in Patients Older Than 80 Years? Clin Orthop Relat Res. 2020;478(8):1741-51.\u003c/li\u003e\n\u003cli\u003eChait A, den Hartigh LJ. Adipose Tissue Distribution, Inflammation and Its Metabolic Consequences, Including Diabetes and Cardiovascular Disease. Front Cardiovasc Med. 2020;7:22.\u003c/li\u003e\n\u003cli\u003eJester R, Rodney A. The relationship between obesity and primary Total Knee Replacement: A scoping review of the literature. Int J Orthop Trauma Nurs. 2021;42:100850.\u003c/li\u003e\n\u003cli\u003eValderr\u0026aacute;bano RJ, Lui LY, Lee J, Cummings SR, Orwoll ES, Hoffman AR, et al. Bone Density Loss Is Associated With Blood Cell Counts. J Bone Miner Res. 2017;32(2):212-20.\u003c/li\u003e\n\u003cli\u003eKristjansdottir HL, Mellstr\u0026ouml;m D, Johansson P, Karlsson M, Vandenput L, Lorentzon M, et al. High platelet count is associated with low bone mineral density: The MrOS Sweden cohort. Osteoporos Int. 2021;32(5):865-71.\u003c/li\u003e\n\u003cli\u003ePolineni S, Resulaj M, Faje AT, Meenaghan E, Bredella MA, Bouxsein M, et al. Red and White Blood Cell Counts Are Associated With Bone Marrow Adipose Tissue, Bone Mineral Density, and Bone Microarchitecture in Premenopausal Women. J Bone Miner Res. 2020;35(6):1031-9.\u003c/li\u003e\n\u003cli\u003eTao L, Jiang W, Li H, Wang X, Tian Z, Yang K, et al. Single-cell RNA sequencing reveals that an imbalance in monocyte subsets rather than changes in gene expression patterns is a feature of postmenopausal osteoporosis. J Bone Miner Res. 2024;39(7):980-93.\u003c/li\u003e\n\u003cli\u003eDalbeth N, Gosling AL, Gaffo A, Abhishek A. Gout. Lancet. 2021;397(10287):1843-55.\u003c/li\u003e\n\u003cli\u003eLin KM, Lu CL, Hung KC, Wu PC, Pan CF, Wu CJ, et al. The Paradoxical Role of Uric Acid in Osteoporosis. Nutrients. 2019;11(9).\u003c/li\u003e\n\u003cli\u003eChen L, Peng Y, Fang F, Chen J, Pan L, You L. Correlation of serum uric acid with bone mineral density and fragility fracture in patients with primary osteoporosis: a single-center retrospective study of 253 cases. Int J Clin Exp Med. 2015;8(4):6291-4.\u003c/li\u003e\n\u003cli\u003eYu J, Xu C, Ma D, Li Y, Yang L. Serum uric acid/creatinine ratio and osteoporosis in the elderly: a NHANES study. Front Med (Lausanne). 2025;12:1530116.\u003c/li\u003e\n\u003cli\u003eBarchetta I, Lubrano C, Cimini FA, Dule S, Passarella G, Dellanno A, et al. Liver fibrosis is associated with impaired bone mineralization and microstructure in obese individuals with non-alcoholic fatty liver disease. Hepatol Int. 2023;17(2):357-66.\u003c/li\u003e\n\u003cli\u003eKim G, Kim KJ, Rhee Y, Lim SK. Significant liver fibrosis assessed using liver transient elastography is independently associated with low bone mineral density in patients with non-alcoholic fatty liver disease. PLoS One. 2017;12(7):e0182202.\u003c/li\u003e\n\u003cli\u003eLi H, Luo H, Zhang Y, Liu L, Lin R. Association of Metabolic Dysfunction-Associated Fatty Liver Disease and Liver Stiffness With Bone Mineral Density in American Adults. Front Endocrinol (Lausanne). 2022;13:891382.\u003c/li\u003e\n\u003cli\u003eNakchbandi IA. Osteoporosis and fractures in liver disease: relevance, pathogenesis and therapeutic implications. World J Gastroenterol. 2014;20(28):9427-38.\u003c/li\u003e\n\u003cli\u003eGuss JD, Taylor E, Rouse Z, Roubert S, Higgins CH, Thomas CJ, et al. The microbial metagenome and bone tissue composition in mice with microbiome-induced reductions in bone strength. Bone. 2019;127:146-54.\u003c/li\u003e\n\u003cli\u003eYan J, Charles JF. Gut Microbiome and Bone: to Build, Destroy, or Both? Curr Osteoporos Rep. 2017;15(4):376-84.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\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":"Total knee arthroplasty, osteolysis, risk factors, biomarkers, BMI, leukocyte count, FIB-4 index, UACR","lastPublishedDoi":"10.21203/rs.3.rs-7633068/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7633068/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e This study aimed to identify early risk factors for periprosthetic osteolysis after total knee arthroplasty (TKA) and establish clinically useful predictive biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A retrospective analysis was conducted on patients who underwent TKA at our institution between January 1, 2018, and October 31, 2023. Initially, 871 patients were screened, and 375 met the inclusion criteria after applying strict eligibility standards. Patients were categorized into an osteolysis group (n=17) and a non-osteolysis group (n=358). Data on baseline characteristics (age, gender, BMI, diabetes, and hypertension history) and postoperative laboratory results were collected. Logistic regression analyses identified independent risk factors for osteolysis, with subgroup analyses also performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Multivariate logistic regression analysis identified elevated leukocyte count (OR=1.47, 95%CI: 1.19-1.80, P\u0026lt;0.001), BMI (OR=1.24, 95%CI: 1.03-1.49, P=0.021), and FIB-4 index (OR=2.08, 95%CI: 1.23-3.52, P=0.006) as significant positive risk factors, while the uric acid-to-creatinine ratio (UACR) was negatively associated (OR=0.51, 95%CI: 0.31-0.82, P=0.006). Subgroup analyses revealed variations in these risk factors across gender, age, diabetes, and hypertension status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study demonstrates that leukocyte count, BMI, FIB-4 index, and UACR are independent risk factors for early periprosthetic osteolysis after TKA. These findings may assist in the early identification and management of high-risk patients, thereby reducing postoperative complications and improving patient outcomes.\u003c/p\u003e","manuscriptTitle":"Systemic Inflammatory and Metabolic Risk Factors for Early Periprosthetic Osteolysis After Total Knee Arthroplasty: A Predictive Biomarker Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-02 23:31:40","doi":"10.21203/rs.3.rs-7633068/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":"84342efe-122b-41fc-98d7-331fd796474d","owner":[],"postedDate":"November 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-05T08:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-02 23:31:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7633068","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7633068","identity":"rs-7633068","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}