Osteoarthritis is a risk factor for renal function injury based on the National Health and Nutrition Examination Survey and Mendelian Randomized study

Abstract

This study aims to investigate the association and causality between osteoarthritis (OA) and chronic kidney disease (CKD) using data from the National Health and Nutrition Examination Survey (NHANES) and Mendelian Randomization (MR) analysis. Participants with OA, urinary albumin, urinary creatinine, urinary albumin-to-creatinine ratio (UACR), blood creatinine, and estimated glomerular filtration rate (eGFR) were selected from NHANES. CKD was calculated using the CKD-EPI equation, and logistic regression assessed by the OA-CKD association. A two-sample MR analysis was conducted using Genome-wide association studies (GWAS) data for OA, hip OA (HOA), knee OA (KOA), acute renal failure (ARF), chronic renal failure (CRF), cystatin C, serum creatinine (eGFRcrea), and microalbuminuria. The inverse variance weighted (IVW) method was used, with heterogeneity, sensitivity, and pleiotropy assessments. The cross-sectional analysis showed a significant positive association between OA and CKD [unadjusted OR: 2.398 (95% CI: 2.176–2.643), p < 0.001], which persisted after adjustment for demographic factors, socioeconomic status, lifestyle factors, and medical history [adjusted OR: 1.161 (95% CI: 1.029–1.310), p = 0.015]. The MR analysis revealed no significant causal relationship between overall OA and renal function markers but found a significant genetic association between HOA and cystatin C [IVW p = 0.0014, OR = 1.02, 95% CI: 1.01–1.03], and between KOA and cystatin C [IVW p < 0.0001, OR = 1.06, 95% CI: 1.04–1.08]. Our study indicates that HOA and KOA are risk factors for renal function injury, providing new insights for clinical OA management.

Introduction

Osteoarthritis (OA) and chronic kidney disease (CKD) are two common chronic conditions that place a significant burden on global healthcare systems^1,2. OA can affect nearly all joints, characterized by cartilage degeneration, synovial changes, and subchondral bone alterations. Its primary symptoms include joint pain, stiffness, and limited mobility^3,4. With the global rise in aging populations, alongside the prevalence of metabolic disorders such as obesity and diabetes, the incidence of OA continues to increase^5,6. It is estimated that over 240 million people worldwide, including more than 32 million in the United States, are affected by OA, particularly in the hip and knee joints. Hip and knee OA often lead to severe disability, significantly impairing mobility and quality of life. In advanced cases, total hip and knee replacement surgeries are frequently required to alleviate pain and restore function, highlighting the substantial burden of OA on individuals and healthcare systems^7,8. However, OA is not confined solely to joint issues; it may also impact multiple organ systems through mechanisms such as systemic inflammation and metabolic dysfunction^9,10.

CKD is another major global health threat, with a prevalence of approximately 11–13%¹¹, and it continues to rise each year¹². The progression of CKD can lead to renal failure, worsening cardiovascular disease, and an increased risk of mortality¹³. Growing evidence suggests that CKD is more prevalent among OA patients^14,15, with studies reporting a prevalence of 61.9% (95% CI: 56.4–66.3) based on eGFR calculated using the Cockcroft-Gault equation¹⁶. although the underlying reasons remain unclear. This may be related to shared risk factors such as smoking, alcohol consumption, obesity, and type 2 diabetes. Therefore, we hypothesize that a potential pathophysiological link exists between OA and CKD. The underlying biological mechanisms may include systemic inflammation, oxidative stress, and the use of non-steroidal anti-inflammatory drugs (NSAIDs), which are commonly prescribed for OA management. These factors may contribute to accelerated renal damage by affecting kidney structure and function.

Given the complex interplay of these risk factors, investigating the relationship between OA and renal function impairment holds significant clinical importance. Cross-sectional studies based on data from the National Health and Nutrition Examination Survey (NHANES) can reveal the statistical correlation between OA and CKD, but they struggle to overcome the influence of confounding factors and the possibility of reverse causality. Therefore, this study further employs the Mendelian Randomization (MR) method, using genetic variants as instrumental variables to more reliably assess the causal effect of OA on renal function impairment. By integrating cross-sectional analysis with genetic epidemiology, this study aims to uncover the potential impact of OA on kidney function, providing a scientific basis for optimizing clinical management and early intervention strategies.

Materials and methods

Study design

This study employed two distinct analytical approaches to investigate the relationship between OA and renal function: (a)Cross-sectional analysis using NHANES data: We conducted a population-based cross-sectional study to assess the association between OA and CKD using data from NHANES cycles (2011–2020). This design allowed us to evaluate the prevalence of OA and CKD, as well as their association, in a large, representative sample of the U.S. population. (b)Two-sample Mendelian Randomization (TSMR) analysis using GWAS data: To explore potential causal relationships, we performed a two-sample MR analysis using summary-level data from published genome-wide association studies (GWAS). This approach leverages genetic variants as instrumental variables to infer causality between OA (including overall OA(AOA), hip OA(HOA), and knee OA(KOA)) and renal function markers, while minimizing confounding and reverse causality.

Cross-sectional study

Data source

NHANES is a population-based cross-sectional survey designed to collect health and nutrition information from the U.S. population, including demographic data, lifestyle factors, and health and nutrition status^17,18. In this study, we selected NHANES cycles from 2011 to 2020 because this time frame provides the most recent and comprehensive data on OA and CKD. These cycles include standardized measurements of key renal function indicators (e.g., urinary albumin, creatinine, and eGFR) and self-reported OA diagnoses, ensuring consistency and reliability in our analysis. Additionally, the inclusion of multiple cycles over this decade ensures a robust and representative sample, enhancing the generalizability of our findings. After merging demographic variables, questionnaire responses, and laboratory test data, a total of 45,462 participants aged over 20 were included from the five selected NHANES cycles. We further excluded participants with missing OA questionnaire data, relevant laboratory tests, or covariates. Ultimately, 22,525 participants were included in this study. Figure 1 illustrates the participant selection flowchart.

Fig. 1.

Flowchart of NHANCES in our study.

Measurements and definitions

Self-reported OA was defined through a questionnaire using the following two questions: "Have you ever been told by a doctor or other healthcare professional that you have arthritis?" and "What type of arthritis do you have?" Participants who answered “yes” were classified as having OA. The quality of the interviews and data entry process was ensured through a computer-assisted personal interview system¹⁹. Five continuous renal function indicators were derived from the NHANES laboratory data as dependent variables: urinary albumin, urinary creatinine, urinary albumin-to-creatinine ratio (UACR), blood creatinine, and estimated glomerular filtration rate (eGFR) based on creatinine levels. Notably, eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation²⁰. CKD was further.

Covariates

In this study, we included sociodemographic factors (e.g., gender, age, race, poverty-income ratio (PIR), Physical activity, education level, body mass index (BMI)), lifestyle factors (e.g., smoking status, alcohol consumption), and medical history (e.g., diabetes) as covariates. These covariates were selected based on their established or potential influence on both OA and CKD. Sociodemographic factors such as PIR, education level, and marital status were included because they are closely associated with healthcare access, health behaviors, and disease prevalence. For instance, lower PIR and education levels are linked to reduced access to healthcare and higher rates of chronic conditions^21,22, while marital status may influence social support and health-seeking behaviors²³. Lifestyle factors like smoking and alcohol consumption were prioritized due to their well-documented roles in systemic inflammation and renal function injury. Smoking is a known risk factor for both OA progression and CKD^24,25, while excessive alcohol consumption can exacerbate renal damage²⁶. Additionally, medical history (e.g., diabetes) was included as it is a shared risk factor for both OA and CKD^27,28, potentially confounding their relationship. Education level was categorized into three groups used by NHANES: less than high school, high school graduate or equivalent, and college or above. Participants were classified into four racial/ethnic groups: non-Hispanic White, non-Hispanic Black, Mexican American, and other races. PIR was divided into three categories: low (< 1.5), middle (1.5–3.5), and high (> 3.5). PIR was categorized into three groups: (1) Low income: PIR < 1.5, indicating income below 1.5 times the poverty threshold; (2) Middle income: PIR 1.5–3.5, indicating income between 1.5 and 3.5 times the poverty threshold; and (3) High income: PIR > 3.5, indicating income above 3.5 times the poverty threshold. Physical activity was categorized into three levels based on self-reported frequency: (a) Regular activities: participants who engaged in moderate or vigorous physical activity at least 3 times per week; (b) Occasional activities: participants who engaged in moderate or vigorous physical activity 1–2 times per week; and (c) Rarely active: participants who engaged in moderate or vigorous physical activity less than once per week. Marital status was defined in three groups: married and living with a partner, other statuses (including widowed, divorced, or separated), and never married. For lifestyle factors, individuals who had smoked more than 100 cigarettes in their lifetime were classified as smokers. Alcohol consumption was assessed using the questions "Did you have at least 12 drinks in the past year?" and "Have you ever had at least 12 drinks in your life?" Diabetes was defined as self-reported diabetes, the use of diabetes medications, or meeting the criteria established by the American Diabetes Association (ADA)²⁹.

Mendelian randomization analysis

Summary-level GWAS data

Data for overall AOA, HOA, and KOA were obtained from the Genetics of Osteoarthritis (GO) Consortium, which conducted a genome-wide meta-analysis of 11 osteoarthritis phenotypes across 13 international cohorts from 9 populations, encompassing a total of 826,690 individuals (including 177,517 OA patients)³⁰. For renal function outcomes, we selected serum creatinine, acute renal failure (ARF), chronic renal failure (CRF), microalbuminuria, and cystatin C as representative markers. Detailed information regarding the sample sizes and variables can be found in Appendix Table 1^31–34.

Table 1.

Demographic and baseline characteristics of OA group versus non-OA group.

	All participants (N = 22,525)	In non-OA group (N = 16,453)	In OA group (N = 6,072)	p-value
Age	47.700 ± 16.978	43.378 ± 15.889	59.909 ± 13.691	< 0.001
Gender, %				< 0.001
Man	48.294	51.51	39.21
Women	51.706	48.49	60.79
Race, %				< 0.001
Mexican Americans	8.593	10.008	4.596
Non-Hispanic whites	65.127	61.87	74.327
Non-Hispanic blacks	10.764	11.051	9.954
other	15.515	17.071	11.123
Poverty to income ratio, %				0.015
< 1.5	25.873	25.856	25.922
1.5–3.5	31.296	30.814	32.659
> 3.5	42.83	43.33	41.42
Educational level, %				< 0.001
High school and lower	13.52	13.053	14.837
High school and equivalent	22.861	22.278	24.506
University and above	63.619	64.668	60.656
Marital status, %				< 0.001
Married and living with a partner	62.446	62.682	61.781
Widowed, divorced or separated	19.349	16.04	28.694
Never married	18.205	21.278	9.526
Smoking status, %				< 0.001
No	56.776	60.144	47.266
Yes	43.224	39.856	52.734
Alcohol consumption status, %				0.001
No	53.425	52.781	55.244
Yes	46.575	47.219	44.756
BMI	29.356 ± 6.983	28.735 ± 6.678	31.110 ± 7.509	< 0.001
Diabetes status, %				< 0.001
No	89.61	92.4	81.73
Yes	10.39	7.6	18.27
Physical activity, %				< 0.001
Regular activities	19.544	22.523	11.131
Occasional activities	35.686	36.473	33.466
Rarely active	44.77	41.005	55.403
Albumin, urine (ug/mL)	34.267 ± 246.212	30.573 ± 236.211	44.701 ± 272.212	0.00015
Urine creatinine (mg/dL)	121.679 ± 80.652	124.572 ± 82.536	113.505 ± 74.473	< 0.001
Albumin-creatinine ratio (mg/g)	33.647 ± 275.374	29.931 ± 283.148	44.141 ± 251.833	0.0007
Creatinine-frozen serum (mg/dL)	0.878 ± 0.340	0.868 ± 0.316	0.905 ± 0.397	< 0.001
eGFR	92.672 ± 21.835	96.547 ± 20.881	81.727 ± 20.729	< 0.001
CKD				< 0.001
No	84.714	88.017	75.386
Yes	15.286	11.983	24.614

BMI: body mass index; CKD: Chronic kidney disease; OA: Osteoarthritis; Values are presented as mean  ±  standard deviation (SD) unless otherwise specified.

Selection of instrument variables

In our MR study, AOA, HOA, and KOA were considered as exposure factors, while the renal function indicators served as outcome variables. We utilized single nucleotide polymorphisms (SNPs) that showed genome-wide significant associations with the exposure factors and exhibited no linkage disequilibrium (LD) (r² < 0.001, kb = 10,000) as instrumental variables (P < 5E-08)^35,36. Considering that smoking, alcohol consumption, obesity, hyperlipidemia, hypertension, and diabetes are risk factors for kidney disease, we searched for SNPs associated with these risk factors using the LDtrait website³⁷, We identified rs1121980 and rs13107325 in AOA, rs67924081 in HOA, and rs1047891, rs10974438, and rs1426371 in KOA that were related to these risk factors, and consequently, we excluded these SNPs to minimize potential confounding effects. Additionally, we excluded SNPs with inconsistent alleles between exposure and outcome, as well as SNPs with intermediate allele frequencies that were palindromic, while avoiding the use of proxy SNPs. After applying these stringent selection criteria, we identified 23 SNPs associated with AOA, 41 SNPs associated with HOA, and 28 SNPs associated with KOA; only these rigorously selected SNPs were used for the final causal analysis.

Statistical analyses

Cross-sectional study

The NHANES data analysis followed the NHANES statistical tutorial, with sample weights applied according to the complex multistage probability sampling design. Continuous variables were presented as mean ± standard deviation, while categorical variables were reported as counts and percentages. Complex sample weights were used to estimate population characteristics, BMI, and the overall prevalence of diabetes and arthritis. Statistically significant covariates were then added to a multivariable logistic regression model for further analysis. In this study, OA was treated as the predictor variable, while CKD was the outcome variable in the association analysis. After adjusting for covariates, we further explored the relationship between OA and CKD, calculating the odds ratio (OR) and its 95% confidence intervals (CIs). To improve result accuracy, we performed subgroup analyses based on age, gender, and physical activity levels. A p-value of < 0.05 was considered statistically significant. All analyses were conducted using R software (version 4.2.3).

TSMR analysis

As an epidemiological statistical method, MR must satisfy three key assumptions: (a) Relevance assumption: The genetic variants used as instrumental variables must be strongly associated with the exposure. (b) Independence assumption: The genetic variants must be independent of any unmeasured confounding factors between the exposure and the outcome. (c) Exclusion restriction assumption: The genetic variants must influence the outcome only through the exposure, not directly³⁸. In this study, the inverse variance weighted (IVW) method was used as the primary analytical approach, while MR-Egger regression and the weighted median estimator (WM) were employed as supplementary methods for two-sample MR analysis. When all selected SNPs are valid instrumental variables, the IVW method provides the most accurate estimate of causal effects³⁹. When more than half of the SNPs are valid, the WM method can yield a reliable causal estimate⁴⁰. In cases where all SNPs are considered invalid, the MR-Egger method can still provide a reliable causal estimate⁴¹.

To assess the reliability of MR results, we used the IVW and MR-Egger regression methods to detect heterogeneity, calculating Cochran’s Q statistic to quantify the degree of heterogeneity; heterogeneity was considered present when the p-value was less than 0.05⁴². We assessed horizontal pleiotropy by calculating the intercept of the MR-Egger regression; a p-value of less than 0.05 for the MR-Egger intercept indicates the presence of horizontal pleiotropy, making the MR results unreliable⁴¹. We employed the MR-PRESSO global test to identify outliers; a p-value of less than 0.05 in the MR-PRESSO global test indicates the presence of outliers, which should be excluded and the MR analysis repeated⁴³. Additionally, leave-one-out analysis was used to evaluate whether the MR results were influenced by any single SNP. Finally, we used the F statistics to assess the strength of the genetic instruments. An F statistic greater than 10 (F = β²/SE²) indicates that the MR results are unlikely to be affected by weak instrumental variables⁴⁴. All MR-related analyses were conducted in R (version 4.2.3) using the TwoSampleMR package⁴⁵, and the MR analysis flow is illustrated in Fig. 2.

Fig. 2.

Flowchart of Mendelian randomization in our study.

Since multiple testing was conducted, we applied the Bonferroni method to adjust the significance threshold (0.05 / (3 × 5) = 0.003). Therefore, for a conclusion to be considered significant in this MR study, the following three criteria must be met: (a) The p-value of the IVW method is < 0.003; (b) The results of the weighted median and MR-Egger regression methods are consistent with the direction of the IVW results; (c) Sensitivity analyses indicate that there is no horizontal pleiotropy, and the MR results are not driven by a single SNP.

Result

Population-based study

This study included a total of 22,525 eligible participants with a mean age of 47.700 years (standard deviation ±16.978), of which 48.294% were male. Among them, 6,072 had OA and 4,359 had CKD. Table 1 presents the demographic and baseline characteristics of the OA and non-OA groups. Notably, the OA group was significantly older than the non-OA group (p < 0.001). The proportion of females with OA (51.706%) was higher than that of males (48.294%). The percentage of non-Hispanic Whites was higher among arthritis patients compared to those without arthritis (74.327% vs. 61.87%). Additionally, individuals with OA had higher rates of poverty, lower education levels, were more likely to be married, smoked, consumed alcohol, had higher BMI, were less physically active, and had higher rates of diabetes (all p < 0.001). Most importantly, the proportion of individuals with impaired renal function and CKD was significantly higher in the OA group compared to the non-OA group (24.614% vs. 11.983%, p < 0.001). Table 2 shows the results of the logistic regression analysis with OA as the independent variable and CKD as the outcome. Overall, OA was significantly positive associated with CKD [OR 2.398 (2.176, 2.643), p < 0.001]. This relationship remained significant after adjusting for demographic factors, socioeconomic status, BMI, smoking, alcohol consumption, and diabetes [OR 1.161 (1.029, 1.310), p = 0.015]. Table 3 displays the results of the stratified analysis, showing that the association between OA and CKD increased with age and decreased physical activity. The final model showed the following relationships between OA and CKD: in the ≥60 age group [OR = 1.162 (1.011, 1.336), p = 0.035], in the group with low physical activity [OR = 1.226 (1.055, 1.424), p = 0.008], and among females with OA [OR = 1.192 (1.015, 1.399), p = 0.032].

Table 2.

Association between chronic kidney disease status and osteoarthritis.

CKD	Crude OR (95%CI)	Model1, OR (95%CI)	Model2, OR (95%CI)	Model3, OR (95%CI)
OA	2.398(2.176,2.643)	1.356(1.207,1.524)	1.274(1.134,1.431)	1.161(1.029,1.310)
Non-OA	Ref	Ref	Ref	Ref

Logistic regression models:

Crude Model: Unadjusted model.

Model 1: Adjusted for age, gender and race.

Model 2: Further adjusted for socioeconomic factors including educational level, poverty to income ratio, physical activity and Marital status.

Model 3: Further adjusted for diabetes, alcohol using, smoking and BMI.

Table 3.

Subgroup analysis of the association of chronic kidney disease and osteoarthritis.

	OR (95%CI)	p-value
Gender
Men
OA	1.166(0.971, 1.400)	0.100
Non-OA	Ref	Ref
Women
OA	1.192(1.015, 1.399)	0.032
Non-OA	Ref	Ref
Physical activity
Regular activities
OA	1.189(0.779, 1.816)	0.423
Non-OA	Ref	Ref
Occasional activities
OA	1.061(0.847, 1.329)	0.604
Non-OA	Ref	Ref
Rarely active
OA	1.226(1.055, 1.424)	0.008
Non-OA	Ref	Ref
Age
< 60
OA	1.756(1.462, 2.108)	< 0.001
Non-OA	Ref	Ref
≥ 60
OA	1.162(1.011,1.336)	0.035
Non-OA	Ref	Ref

All data were adjusted for gender, age, race, education level and poverty to income ratio, physical activity and Marital status, diabetes, alcohol using, smoking and BMI.

MR framework analyses

Causal effects of AOA, HOA, and KOA on renal function

When AOA, HOA, and KOA were used as exposure factors, and after controlling confounding factors such as hypertension, hyperlipidemia, diabetes, smoking, alcohol consumption, and obesity, we identified 23, 41, and 28 validated SNPs, respectively. According to Figure 3, no significant causal association was found between AOA and the various renal function indicators. However, a significant genetic association was observed between HOA and cystatin C [IVW p = 0.0014, OR = 1.02, 95% CI = 1.01–1.03], as well as between KOA and cystatin C [IVW p < 0.0001, OR = 1.06, 95% CI = 1.04–1.08]. No significant associations were found between HOA and ARF, CRF, serum creatinine (eGFRcrea), or microalbuminuria, nor between KOA and ARF, CRF, serum creatinine (eGFRcrea), or microalbuminuria. The results from the weighted media and MR-Egger methods were consistent with those of the IVW method, indicating an increased risk (scatter plots are available in the supplementary materials). The Cochran’s Q test for both IVW and MR-Egger methods yielded p-values greater than 0.05, indicating no heterogeneity (funnel plots are available in the supplementary materials). Similarly, the p-values from the MR-PRESSO and MR-Egger intercept tests were also greater than 0.05, suggesting no pleiotropy. Additionally, leave-one-out analysis showed that the associations between HOA, KOA, and cystatin C were not driven by any single SNP (leave-one-out plots are available in the supplementary materials).

Fig. 3.

MR estimates derived from each method of assessing the causal effect of OA on renal function indices.

Combining the results of NHANES and MR analysis, we observed a significant association between OA and CKD in a cross-sectional study, suggesting a potential link between these two diseases. However, in the MR analysis, significant associations were only observed between HOA and KOA and cystatin C. Therefore, we speculate that OA may be a potential risk factor for renal function injury, but further studies are needed to verify these findings and explore their causal relationship in depth.

Discussion

This study, using NHANES data combined with the MR method, is the first to explore the potential causal relationship between OA and renal function injury. The results revealed a significant positive statistical association between OA and CKD in the NHANES sample, suggesting that individuals with OA face a higher risk of CKD. However, this association does not directly prove causality between the two conditions. Further MR analysis indicated that not all types of OA are significantly associated with renal function impairment, but specific types, such as HOA and KOA, showed a significant causal relationship with kidney damage. This may be attributed to several factors. First, AOA encompasses a heterogeneous group of joint conditions, and the genetic variants associated with OA may not uniformly influence renal function. Second, the sample size for AOA in the GWAS data may have been insufficient to detect a causal effect, particularly if the genetic variants have weak or moderate effects on renal function. Future studies with larger sample sizes and more refined OA phenotypes are needed to further explore this relationship.

Although our study suggests a potential association between HOA and KOA with renal function impairment, it is important to recognize that the underlying mechanisms remain unclear. To further explore these mechanisms, we analyzed relevant literature to identify possible factors that increase the risk of renal damage in OA patients and provide new insights for the clinical management of OA. The mechanisms linking OA and renal function impairment are complex, involving multiple pathophysiological processes. Several hypotheses have been proposed by researchers. First, NSAIDs, commonly used in the clinical treatment of OA, may contribute to renal damage. However, this remains controversial. On one hand, NSAIDs inhibit prostaglandin synthesis, impairing renal blood flow regulation, which reduces renal blood flow and decreases glomerular filtration rate, potentially inducing or exacerbating kidney injury⁴⁶. Some researchers have suggested that NSAIDs may be a potential risk factor for end-stage renal disease, though clear direct evidence is still lacking⁴⁷. On the other hand, some studies indicate that topical NSAIDs are safe and effective for OA patients, without significant adverse renal effects⁴⁸. Additionally, recent research shows that the long-term use of NSAIDs in arthritis patients has a limited impact on cardiovascular and renal events under modern medical conditions⁴⁹. Second, the systemic inflammation hypothesis is another important mechanism linking OA to renal impairment. Inflammatory markers such as IL-6, IL-17, and TNF-α are commonly elevated in OA patients, contributing not only to local joint inflammation but also potentially damaging renal microvascular systems^50,51. Furthermore, reduced physical activity is common among OA patients. Joint pain or fear of disease progression may lead patients to limit their exercise, which further promotes endothelial dysfunction and other pathological changes that exacerbate renal damage⁵², This finding is consistent with our study results. Finally, the oxidative stress hypothesis suggests that OA leads to elevated levels of reactive oxygen species (ROS), a key factor in kidney injury. ROS are produced in both OA and CKD, leading to renal cell damage and further affecting renal function⁵³. Our findings are consistent with previous studies suggesting that OA is a potential risk factor for CKD, which further adds to the credibility of our findings^54,55. In summary, osteoarthritis may contribute to renal impairment through multiple mechanisms, including the use of NSAIDs, systemic inflammation, reduced physical activity, and oxidative stress.

Although OA itself affects renal function through mechanisms such as systemic inflammation and oxidative stress, total hip and knee arthroplasty, as a common treatment for advanced OA, can also significantly impact renal function^56–58. Therefore, it is essential to consider the potential effects of surgical intervention on kidney function as part of OA treatment. Analyzing the impact of surgery on renal function through relevant literature can provide guidance for the prevention of renal impairment in clinical settings. First, intraoperative hypotension is a critical factor affecting renal perfusion. Hemorrhage can lead to a reduction in overall blood volume, and when the mean arterial pressure falls below 65 mm Hg for an extended period, the kidney’s autoregulatory function may be compromised, potentially leading to postoperative acute kidney injury (AKI)⁵⁹. Second, intraoperative blood transfusions may increase the risk of postoperative AKI; although the specific mechanisms are not fully understood, studies indicate an independent association between transfusion and the occurrence of AKI⁶⁰. Furthermore, improper fluid management during surgery may negatively impact renal function, especially regarding the use of colloidal solutions, although the evidence remains debated^61,62. The duration of surgery is also a significant factor; research has shown that when surgical time exceeds 120 min, the incidence of AKI nearly doubles ⁶³, often associated with substantial intraoperative blood loss, coagulopathy, and the complexity of the surgical site. Additionally, while NSAIDs are widely used for postoperative analgesia, their potential nephrotoxic effects—through the inhibition of prostaglandin synthesis, reduction of renal blood flow, and decrease in eGFR—may increase the risk of AKI⁶⁴. Postoperative hypoalbuminemia is also considered a risk factor for AKI⁶⁵. Moreover, the use of antibiotic-impregnated bone cement may contribute to the risk of AKI. Jafari et al.⁶⁶ pointed out that antibiotic-impregnated bone cement contains aminoglycosides and/or vancomycin, both of which are nephrotoxic. McGlothan et al.⁶⁷ also found that AKI patients exhibited rapid recovery of renal function following the removal of high-dose antibiotic bone cement, suggesting that the use of high-dose cement may be a contributing factor to AKI. It is also noteworthy that pre-existing renal insufficiency is a risk factor for postoperative kidney damage⁶⁸, particularly as elevated creatinine levels significantly affect renal filtration capacity⁶⁹. Even a slight increase in plasma creatinine, if left unaddressed, can lead to progressive renal failure and increased mortality⁷⁰. Studies have shown that patients with pre-existing CKD have a significantly higher incidence of AKI following surgery. For instance, Porter et al.⁷¹ reported that patients with a preoperative eGFR of less than 30 mL/min per 1.73 m² have a 2.4-fold increased risk of developing AKI compared to those with normal renal function, and the incidence of AKI in stage 3 CKD patients can reach as high as 16.7%, significantly prolonging hospital stays⁷².

Based on the above findings, we propose several new insights and strategies for the clinical treatment of OA. Our study highlights the significant association between HOA and KOA with renal function injury, which has important implications for the screening and management of OA patients. First, clinicians should consider regular renal function screening for patients with HOA or KOA, particularly those with additional risk factors such as advanced age, obesity, or diabetes. Early detection of renal injury could facilitate timely interventions to slow disease progression. We recommend that OA treatment should be individualized⁷³, taking into account the patient’s specific clinical characteristics, comorbidities, and risk factors for renal injury. An individualized strategy can be developed through a comprehensive assessment that includes: (a) patient history and comorbidities, such as diabetes, hypertension, or pre-existing CKD, which may influence treatment choices; (b) severity of OA symptoms, including pain levels and functional limitations, to determine the most appropriate therapeutic approach; (c) renal function status, assessed through routine monitoring of eGFR and urinary albumin-to-creatinine ratio (UACR), to guide the selection of medications with minimal renal toxicity; and (d) lifestyle factors, such as physical activity levels and dietary habits, which can be modified to improve overall health and reduce systemic inflammation. Based on this assessment, safer alternative medications or topical treatments can be selected to reduce renal burden. For example, in patients with mild OA and no significant renal injury, topical NSAIDs may be preferred over oral NSAIDs to minimize systemic exposure and renal risk. In patients with moderate to severe OA and impaired renal function, non-NSAID analgesics (e.g., acetaminophen) or disease-modifying agents (e.g., glucosamine) may be considered. Additionally, anti-inflammatory medications should be used judiciously to manage systemic inflammation, and moderate exercise should be encouraged to improve joint function and overall health. Increasing the intake of antioxidants, such as through dietary modifications or supplements, may also help mitigate oxidative stress and its impact on renal function. For patients with end-stage hip or knee osteoarthritis who require total hip or knee replacement, a preoperative renal function assessment is recommended, with particular attention to those with pre-existing renal insufficiency, allowing for targeted preventive measures. During surgery, blood pressure and blood volume should be closely monitored to avoid renal hypoperfusion due to intraoperative hypotension. The cautious use of antibiotic-impregnated bone cement is also necessary, given its potential nephrotoxicity. Moreover, efforts should be made to reduce operative time to lower the incidence of postoperative AKI. By implementing these measures, we aim to effectively manage the risk of renal function injury in OA patients.

However, this study has several limitations. First, due to the constraints of the NHANES data source, it was not possible to specifically investigate the correlation between total hip and knee replacement and CKD, nor the effects of different types of osteoarthritis on CKD. Second, the data on OA primarily relied on patient recall questionnaires, which may introduce recall bias and affect the accuracy of the data. Additionally, a substantial number of participants (n = 19,184) were excluded because they responded ‘unknown’ or had missing data to the arthritis-related questions, potentially limiting the generalizability of our findings. Additionally, the sample in this study was limited to European populations, which may restrict the generalizability of the results to other populations. Future research should consider including data from diverse population samples to enhance the generalizability of the findings and explore the relationship between OA and CKD across different ethnic groups. Furthermore, conducting prospective cohort studies would help overcome the limitations of the current research and provide more direct causal evidence, particularly regarding the association between total hip and knee replacement and CKD. Lastly, future studies should further investigate the underlying mechanisms linking OA and total hip and knee replacement to CKD, especially the pathophysiological processes affecting renal function, to provide stronger theoretical support for clinical interventions.

In summary, this study suggests that HOA and KOA may be risk factors for renal function injury, as indicated by both NHANES and MR analyses. These findings highlight the potential impact of total hip and knee replacement as a treatment for advanced OA on renal function. However, further research is needed to explore the underlying mechanisms and provide stronger evidence to support clinical prevention and treatment strategies.

Author contributions

P and KW were responsible for writing the manuscript, YZ was responsible for the production of Figs. 1-3, QW and ZZ were responsible for the production of Tables 1-3, and L and B were responsible for reviewing and revising the manuscript.

Funding

The effect of Bu Yang Hui Wu Tang combined with wrist and ankle acupuncture on the prevention of postoperative delirium after arthroplasty for hip fracture in elderly people, Funding Project Number :2022FSYYZQ22.

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Competing interests

The authors declare no competing interests.

Moral declaration

Data from the NHANES database and the GAW database were used in this study. The NHANES data were collected in accordance with U.S. federal regulations and Centers for Disease Control and Prevention (CDC) ethical guidelines, and all participants provided informed consent. The GAW data were used in accordance with the appropriate data use protocols and privacy protections. The analysis of this study involved only de-identified data, ensuring participant privacy and data confidentiality.

Institutional review board statement

This study used summary data published by multiple GWAS; the review board statement can be found in the original work. Informed Consent Statement: Patient consent was obtained by corresponding studies, which have been cited in the manuscript.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-97756-z.