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. 2025 Jul 25;37(1):232. doi: 10.1007/s40520-025-03138-w

The causal link between osteoarthritis and falls: the mediating role of paracetamol

Jian Huang 1,#, Xiaodong Hu 1,#, Tianrui Wang 1, Yingze Zhang 1,✉, Lu Wang 2,✉
PMCID: PMC12296754  PMID: 40711723

Abstract

Background

Some observational studies have indicated that osteoarthritis (OA) is a fall risk factor. However, the causal relationship between OA and falls remains unclear.

Methods

We conducted a two-step Mendelian randomization (MR) to investigate the causal relationships between OA, paracetamol use, and the risk of falls and to estimate the mediating effect of paracetamol use. Publicly accessible data for hip OA, knee OA, paracetamol use, and falling risk were sourced from Genome-Wide Association Studies (GWAS).

Result

In the forward MR analysis, the Inverse Variance Weighted (IVW) method indicated that a genetic predisposition to hip OA was significantly associated with an increased risk of falls (OR: 1.053, 95% CI: 1.014 to 1.094, P = 0.007). Consistent directions of effect were observed across other MR analysis methods. The mediated proportion of paracetamol use on the relationship between hip OA and falling risk was 21.70% (β = 0.0113, 95% CI: 5.73–37.8%, P = 0.0078). No causal relationship was observed between knee OA and falling risk, nor between knee OA and the use of paracetamol. In the reversed MR analysis, the IVW method showed that a genetic predisposition to falls may not increase the risk of developing hip and knee OA.

Conclusion

These results provide genetic evidence for a causal effect of hip OA on fall risk, partially mediated by paracetamol, and underscore the importance of fall prevention education and cautious paracetamol use for patients with hip OA.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40520-025-03138-w.

Keywords: Osteoarthritis, Falls, Paracetamol, Genetic variants, Mendelian randomization

Introduction

Osteoarthritis (OA) is one of the most common disabling joint diseases globally, often affecting the knees, hips, and hands [1]. Population epidemiological studies have shown that there were approximately 303.1 million hip and knee OA cases worldwide by 2017 [2]. With the aging of the population, the incidence continues to rise, severely affecting individual quality of life and increasing the global economic burden [3]. OA is a whole-joint disease characterized by progressive cartilage degeneration, synovitis infrapatellar fat pad inflammation, and subchondral bone remodeling, among other features. Its multifactorial etiology involves mechanical forces and biological processes [4, 5]. Patients with OA typically exhibit clinical manifestations such as joint pain, stiffness, and limited mobility, and may also experience a decline in muscle strength and proprioceptive sensation [6]. These issues can affect the patient’s balance and gait, thereby increasing the risk of falls [7]. Contemporary management strategies for OA remain predominantly focused on palliative symptom control, with no disease-modifying agents currently validated to impede the natural course of joint degeneration [8].

Statistics reveal that falls are the predominant cause of unintentional fatal injuries for individuals aged 65 and older in the United States. Alarmingly, the mortality rate attributed to falls in this demographic has seen a consistent rise, with a 41% increase over the past decade—climbing from 55.3 per 100,000 in 2012 to 78.0 per 100,000 by 2021 [9]. For seniors, the likelihood of falls is exacerbated by advancing age, frailty, the incidence of acute or chronic health conditions, and the consumption of specific medications. In light of these alarming figures, identifying effective fall prevention strategies that target controllable risk factors is paramount [10, 11].

Although some studies have reported causal analyses of the relationship between OA and a variety of diseases or indicators [12, 13], there is currently a lack of research on the causal relationship between OA and falls. An increasing number of clinical observational studies have found a close relationship between OA and falls, but the results are inconsistent due to the presence of various confounding factors [14–16]. it is currently unclear whether OA increases the risk of falls due to the use of pain medications such as paracetamol. A study found that people with clinical knee OA are at a higher risk of falling again, and this relationship is mediated by pain medication, especially opioids [17]. To address this gap, we employed advanced genetic methodologies to explore the causal relationship between OA and falls, and to elucidate the mediating effect of paracetamol use. Mendelian randomization (MR) studies leverage genetic variations as instrumental variables (IVs) to investigate the causal links between various exposures and outcomes. The underlying principle involves utilizing the natural assignment of genotypes and their subsequent impact on phenotypes to deduce the influence of biological factors on disease development. This approach circumvents the biases associated with confounding variables and the issue of reverse causality [18].

In this study, we utilized a two-step MR approach to examine the potential causal link between two-site OA, encompassing both hip and knee OA, and the occurrence of falls. Given the significant association between OA and paracetamol usage for pain management, we conducted a two-step MR analysis to determine if the influence of OA on falls is mediated by paracetamol use.

Materials and methods

Study design

First, a bidirectional MR was used to explore the causal link between OA (hip and knee OA) and the propensity for falls. Then, a two-step MR design was employed to test the causal mediating effect of paracetamol use on the relationship between OA and falls [19]. In the first step, MR was used to establish the causal effect of the OA on the potential mediator paracetamol (β1). In the second step, IVs for paracetamol were used to assess the causal effect of potential mediators on the risk of falls (β2). The total effect of OA on falls calculated above was labeled β3. Finally, We assessed the mediating effect and its standard error using both the product of coefficients method (β1 × β2) and the Delta method [20]. Ultimately, we determined the extent to which the effect of OA on falls was mediated by paracetamol use by computing the ratio of the mediating effect, represented by the product of the coefficients (β1 × β2), to the overall effect (β3) [21]. Figure 1 illustrates the schematic flow of our MR study. Given that the data were sourced from publicly accessible databases, no ethical approval was necessary.

Fig. 1.

Fig. 1

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Research flowchart. SNP: Single Nucleotide Polymorphism; OA: osteoarthritis

Data sources

This MR study utilized four distinct datasets sourced from a publicly accessible summary IEU Open Genome-Wide Association Studies (GWAS) project. The detailed information is in Table 1. In the context of our research, The study population came from UK Biobank data which was comprised of 378,169 controls and 24,955 cases for knee OA, 378,169 controls and 15,704 cases for hip OA [22], and 362,103 controls and 89,076 cases for falls individuals [23]. For paracetamol medication, this dataset consisted of 85,782 controls and 377,151 cases. All participants in the datasets are of European descent.

Table 1.

Characteristics of the GWAS included in the MR study

Trait GWAS ID Sample size nSNPs Population Year
Knee osteoarthritis ebi-a-GCST007090 403,124 29,999,696 European 2019
Hip osteoarthritis ebi-a-GCST007091 393,873 29,771,219 European 2019
Paracetamol ukb-b-8147 462,933 9,851,867 European 2018
Falling risk ebi-a-GCST90012857 451,179 7,720,247 European 2020
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Selection of IVs

The selection of IVs was based on three core criteria: 1) There must be a robust and statistically significant association between the IVs and the exposure. 2) The IVs should be free from any confounding factors that might affect the outcome. 3)The genetic variant should exert its influence on the outcome exclusively through its effect on the exposure. We identified Single Nucleotide Polymorphisms (SNPs) that showed a significant association with the exposure variables, surpassing a stringent genome-wide significance threshold (P < 5 × 10− 8) [24]. We also ensured that these SNPs were not in linkage disequilibrium, as evidenced by a low r2<0.001 within a 10,000 kb clumping window based on a 1000-genomes European reference panel [25]. Data were harmonized to maintain analytical consistency and accurately trace the causal pathway between the exposure and outcome through the SNPs. In instances where shared SNPs between the exposure and outcome were not available, we utilized proxy SNPs with a high degree of linkage disequilibrium (r² >0.8) that were significantly associated with the exposure as substitutes. The proportions of trait variance explained by the identified SNPs were calculated using the following formula: R2 = 2 × b2 × MAF × (1 − MAF)/(2 × MAF × (1 − MAF) × b2 + 2 × MAF × (1 − MAF) × N × se(b)2). In addition, we evaluated instrument strength using the F statistic, where F = (R2 × (N − k − 1))/k(1 − R2 ), to test the significant association of the genetic instruments with the exposure, we utilized the criterion of an F-statistic value greater than 10 to exclude weak instrumental variables [26].

Statistical analyses

To investigate causal relationships, we employed four well-established MR methods: Weighted Median, MR-Egger, Weighted Mode, Inverse Variance Weighted (IVW) regression (fixed effects model and multiplicative random effects model). The IVW method is the primary approach in our research [27]. This method is known to provide precise estimates when there is no significant heterogeneity across studies or when horizontal pleiotropy—where the genetic instrument affects the outcome through multiple pathways—is not present [28]. Heterogeneity was assessed using Cochran’s Q test. When the test results suggest substantial heterogeneity (p < 0.05), we would switch from a fixed effects model to a multiplicative random effects model to account for the variability between studies [29]. Furthermore, the MR-Egger regression intercept term was utilized to assess the potential for horizontal pleiotropy, an intercept not significantly different from zero (p > 0.05) indicates the absence of horizontal pleiotropy [30, 31]. All statistical analyses were conducted in R version 4.0.3, leveraging the TwoSampleMR package (version 0.5.5) for the MR analysis.

Results

Causal effects of hip OA on the risk of falling

Initially, we identified 26 SNPs associated with hip OA to serve as IVs. During the harmonization process of the exposure and outcome SNPs, we detected three palindromic SNPs (rs12901372, rs2929451, and rs34687269) that could not be accurately analyzed due to the inability to determine the direction of variation, and subsequently excluded them from our analysis. Each SNP exhibited an F-statistic value that surpassed 10 (Supplementary Table 1). The outcomes of the four MR analysis methods are depicted in Fig. 2. Since Cochran’s Q statistics test identified a statistical heterogeneity (Cochran’s Q = 105.9, p = 5.83E-13, Table 2). The IVW(multiplicative random effects model) method revealed a positive association between hip OA and the risk of falling (Odds Ratio [OR]: 1.053, 95% Confidence Interval [CI]: 1.014 to 1.094, P = 0.007). Consistent directions of effect were observed across the other MR analysis methods. The MR analysis is further illustrated through a forest and scatter plot (Fig. 3A and B). A leave-one-out analysis confirmed that the genetically predicted causal relationship between hip OA and the risk of falling was not influenced by any single SNP (Fig. 3A). Furthermore, sensitivity analyses, including a test for horizontal pleiotropy, did not indicate any significant deviation (Table 2; Fig. 3D).

Fig. 2.

Fig. 2

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The results of MR analysis indicate the causal relationship between hip OA, paracetamol, and falling risk. OR: odds ratio, nsnp: number of snp

Table 2.

Heterogeneity and Pleiotropy testing

Exposure Outcome Heterogeneity test Pleiotropy test
MR Egger IVW MR Egger
Cochran’s Q p Value Cochran’s Q p Value Egger intercept p Value
Osteoarthritis(knee) Paracetamol 1.833 0.7665 2.232 0.8162 0.0012 0.562
Osteoarthritis(knee) Falling risk 1.036 0.9043 1.809 0.8749 0.009 0.429
Osteoarthritis(hip) Paracetamol 104.7 4.243E-13 105.9 5.83E-13 0.00079 0.622
Paracetamol Falling risk 25.66 0.012 26.72 0.0136 0.0055 0.495
Osteoarthritis(hip) Falling risk 56.5 0.00004 57.78 0.00005 0.0045 0.498
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Fig. 3.

Fig. 3

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The causal connections between hip OA and falling risk

Causal effects of falling risk on hip OA

We selected 2 SNP from falling risk to serve as Ivs (Supplementary Table 2). The IVW (fixed effects model) indicated that there was no significant correlation between falling risk and hip OA (OR: 1.374, 95% CI: 0.368–5.188, P = 0.636). As depicted in Fig. 2.

Mediation analyses

We conducted a two-step MR analysis to determine if the identified association between hip OA and an increased risk of falling is mediated by the use of the medication paracetamol. Initially, we investigated the causal relationship between hip OA and paracetamol usage. While harmonizing the exposure and outcome SNPs, we identified three palindromic SNPs—rs12901372, rs2929451, and rs34687269—that were challenging to analyze accurately because we couldn’t ascertain the direction of variation. As a result, we chose to omit these SNPs from our study (Supplementary Table 3). The results from four MR methods are depicted in Fig. 2. The IVW (multiplicative random effects model) method indicated that individuals with hip OA are more likely to use paracetamol (OR: 1.012, CI: 1.003–1.022, P = 0.008, Fig. 2). The findings were further displayed in both the forest plot (Fig. 4A) and scatter plot (Fig. 4B). None of the SNPs were overly sensitive or had a disproportionate influence on the results (Fig. 4C). Additionally, there was no indication of pleiotropy among the SNPs, as noted in Table 2 and depicted in Fig. 4D.

Fig. 4.

Fig. 4

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The causal relationship between hip OA and paracetamol use

Subsequently, we evaluated the causal impact of paracetamol use on the risk of falling. We utilized 14 SNPs associated with paracetamol as IVs (Supplementary Table 4). There was strong causal evidence supporting the association between paracetamol use and an increased risk of falling, as demonstrated by the IVW (multiplicative random effects model) method (OR: 2.478, 95% CI: 1.593–3.876, P = 0.005, Fig. 2). All four MR methods yielded consistent results, which were visually represented in the forest plot (Fig. 5A) and scatter plot (Fig. 5B). No SNP was found to be overly sensitive or disproportionately influential regarding the outcome (Fig. 5C). Additionally, no evidence of pleiotropy among the SNPs was observed (Table 2; Fig. 5D).

Fig. 5.

Fig. 5

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The causal relationship between paracetamol use and falling risk

Mediation effects of paracetamol

The results of the MR analysis revealed that hip OA increases the risk of falling. The analysis also indicated that the paracetamol use mediated 21.70% of the relationship between hip OA and falling risk (β = 0.0113, 95% CI: 5.73–37.8%, P = 0.0078). Further details are shown in Table 3.

Table 3.

The proportion of the increased falling risk due to hip OA that is mediated by Paracetamol use

Exposure Mediator outcome Mediated effect Mediated proportion p Value
Osteoarthritis (hip) Paracetamol Falling risk

0.0113

(0.0297, 0.0196)

21.7%

(5.73%, 37.8%)

 0.007777949
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Causal effects of knee OA on falling risk

We identified 8 SNPs associated with knee OA that were utilized as IVs. In the harmonization process of aligning the exposure and outcome SNPs, we encountered 2 palindromic SNPs, rs17567417 and rs6499244, which exhibited the inability to accurately determine the direction of variation. These were carefully excluded from our subsequent analysis (Supplementary Table 5). The results of the four MR analysis methods are shown in Fig. 6. Cochran’s Q statistics test, applied to our data using the MR-Egger and IVW method, revealed no significant heterogeneity (Cochran’s Q = 1.809, p = 0.8749, Table 2). The IVW method, employing a fixed-effect model, indicated no substantial association between knee OA and the risk of falling (OR: 0.982, 95% CI: 0.928–1.038, P = 0.527). These results were also shown in the forest plot and scatter plot (Supplementary Fig. 1A and 1B). None of the SNPs exerted an outsized influence on the outcome, which rules out any over-sensitivity in our analysis (Supplementary Fig. 1C). Additionally, we found no evidence of pleiotropy among the SNPs ( Supplementary Fig. 1D, Table 2).

Fig. 6.

Fig. 6

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The results of MR analysis indicate the causal relationship between knee OA, paracetamol, and falling risk. OR: odds ratio, nsnp: number of snp

Causal effects of knee OA on the paracetamol use

After excluding 2 palindromic SNPs, a total of 6 SNPs were ultimately included for subsequent analysis. The F statistic for each SNP exceeded 10 (Supplementary Table 6). The results of the four MR analysis methods are presented in Fig. 6. The IVW (fixed effects model) method indicated no significant causal relationship between knee OA and paracetamol use (OR: 1.001, 95% CI: 0.991–1.011, P = 0.904). No heterogeneity or pleiotropy was observed in our study (Supplementary Fig. 2A-D).

Causal effects of falling risk on knee OA

In the reverse MR analysis, we ultimately identified 2 valid SNPs as genetic IVs for falls to predict the risk of developing knee OA (Supplementary Table 7). The IVW (fixed effects model) suggested no significant correlation between the risk of falling and the onset of knee OA (OR: 1.386, 95% CI: 0.831–2.311, P = 0.211). This finding is visually represented in Fig. 6.

Discussion

According to statistics, in the United States, the economic burden of falls and OA reached $87 billion and $84 billion respectively in 2016, and this financial strain continues to grow [32]. While both observational and genetic studies have identified a link between OA and falls, the nature of their causal relationship remains unclear. In this study, we explored the potential causal links between OA of the hip and knee, paracetamol use, and the risk of falling using different MR method. The results from our MR analysis suggest that it is hip OA, rather than knee OA, that is associated with an increased risk of falling. In the reverse MR analysis, we found no evidence to support a causal relationship between falls and OA. Interestingly, further mediation MR analysis revealed that paracetamol use partially mediates the effect of hip OA on the risk of falls.

Some previous observational studies have yielded inconsistent results when exploring the relationship between OA and the risk of falling. Some studies have reported that radiographic or symptomatic OA in the knee and hip increases the risk of falling and subsequent fractures [33–37]. However, some studies indicate that symptomatic knee OA reduces the risk of fractures, while radiographic hip OA in female patients may lower the risk of falling [38, 39]. Hip OA has been identified as a risk factor for falls, and it is associated with an increased annual prevalence of falls in the elderly compared to national estimates [40, 41]. This is consistent with our study. Hip OA may lead to pain and mobility issues, both of which are known risk factors for falls. Moreover, individuals with hip OA may be more prone to falling due to limited range of motion and decreased muscle strength in the joint [42, 43]. Our study further clarifies that this relationship may be partially mediated through the use of paracetamol by patients to alleviate pain. A clinical study has indicated that nonsteroidal anti-inflammatory drugs (NSAIDs) such as aspirin can increase the risk of accidental falls in the elderly [44]. A recent report has also established a link between the use of opioid medications and an increased risk of severe falls [45].

Paracetamol, also known as acetaminophen, is a widely used over-the-counter analgesic and is commonly utilized in patients with OA [46, 47]. Controversy exists regarding the effectiveness of paracetamol for treating OA. A meta-analysis of randomized controlled trials indicated that paracetamol is a proven effective analgesic for alleviating pain associated with OA. Despite its lower potency compared to nonsteroidal anti-inflammatory drugs (NSAIDs), its superior safety makes it the preferred initial treatment. NSAIDs should be reserved for those patients who do not find relief with paracetamol as the first-line therapy [48]. The American Academy of Orthopaedic Surgeons (AAOS) strongly supports the use of paracetamol for treating OA [49]. However, the American College of Rheumatology (ACR) only conditionally recommends paracetamol for people with OA, noting that its effectiveness may be limited, especially as a long-term single treatment [50]. The Osteoarthritis Research Society International (OARSI) diverged from these recommendations by not endorsing paracetamol for any patients, citing concerns over its low efficacy and the minor risk of hepatotoxicity [51]. A study reported that paracetamol might increase risk-taking behavior among healthy young adults, potentially due to a reduction in risk perception [52]. Our research findings suggest that the use of paracetamol could be associated with an increased risk of falls, possibly due to the drug’s sedative effects, which may impair patients’ alertness and balance abilities.

It’s worth noting that not all patients with knee OA are at an increased risk of falls. For instance, some studies have indicated that only those patients with knee symptoms, rather than those with changes detected solely through imaging, are at an increased risk of falls and fractures [33]. Our analysis did not observe a significant causal relationship between knee OA and the risk of falls, nor between knee OA and the use of paracetamol. There are indeed differences in the pathogenesis and clinical manifestations between hip and knee OA, which may lead to different demands for pain management and patterns of medication use [53, 54]. Compared with patients with knee OA, those with hip OA may be more directly affected in terms of balance and walking ability [55, 56].

Additionally, emphasizing fall prevention education for patients and their families is crucial. Our findings provide valuable insights into strategies for raising awareness and preventing fall risks among OA patients, particularly those with hip OA who are prescribed paracetamol for pain relief. This study underscores the need for clinicians to carefully weigh the benefits and risks of paracetamol use in hip OA patients, highlighting the importance of considering alternative pain management strategies and emphasizing fall prevention measures.

However, our study has certain limitations. Since the MR analysis relies on genetic variants as IVs, our findings need to be further validated through clinical trials to enhance the credibility of the results. In addition, our data may be influenced by unmeasured confounding factors, which could affect the interpretation of the relationship between paracetamol use and the risk of falls.

In summary, our study provides novel insights into the complex interplay between hip OA, paracetamol use, and the risk of falling. We demonstrate that hip OA is associated with an increased risk of falling, partially mediated by paracetamol use, which has not been previously elucidated. Future research should further explore these relationships and assess effective strategies to reduce the risk of falling in patients with hip OA, focusing on optimizing pain management and enhancing fall prevention measures.

Conclusion

In our study, the genetic evidence we have presented supports the notion that an increase in hip OA is causally linked to a higher risk of falls, with the effect being partially mediated by paracetamol use.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (1.2KB, csv)
Supplementary Material 2 (9.4KB, csv)
Supplementary Material 3 (11.1KB, csv)
Supplementary Material 4 (5.5KB, csv)
Supplementary Material 5 (3.2KB, csv)
Supplementary Material 6 (3.7KB, csv)
Supplementary Material 7 (1.2KB, csv)
Supplementary Material 8 (16.5MB, tif)
Supplementary Material 9 (16.5MB, tif)

Author contributions

Jian Huang: Writing– original draft, Software, Methodology, Formal analysis. Xiaodong Hu: Writing– original draft, Software. Tianrui Wang: Software, Methodology, Formal analysis. Yingze Zhang: Resources, Formal analysis, Conceptualization. Lu Wang: Writing– review & editing, Funding acquisition, Conceptualization. Jian Huang and Xiaodong Hu contributed equally to this work and are co-first authors. All authors read and approved the final manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The data underpinning the findings can be accessed through the IEU Open GWAS project website, which is located at https://gwas.mrcieu.ac.uk/.

Declarations

Ethical approval

GWAS belongs to public databases. The patients involved in the database have obtained ethical approval. Our study is based on open-source data without the need for additional ethical approval.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jian Huang and Xiaodong Hu contributed equally to this work.

Contributor Information

Yingze Zhang, Email: acadyzzhang@126.com.

Lu Wang, Email: 18872124029@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (1.2KB, csv)
Supplementary Material 2 (9.4KB, csv)
Supplementary Material 3 (11.1KB, csv)
Supplementary Material 4 (5.5KB, csv)
Supplementary Material 5 (3.2KB, csv)
Supplementary Material 6 (3.7KB, csv)
Supplementary Material 7 (1.2KB, csv)
Supplementary Material 8 (16.5MB, tif)
Supplementary Material 9 (16.5MB, tif)

Data Availability Statement

The data underpinning the findings can be accessed through the IEU Open GWAS project website, which is located at https://gwas.mrcieu.ac.uk/.

Articles from Aging Clinical and Experimental Research are provided here courtesy of Springer

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