Pre-Operative Adiposity and Synovial Fluid Inflammatory Biomarkers Provide a Predictive Model for Post-Operative Outcomes Following Total Joint Replacement Surgery in Osteoarthritis Patients
1
Institute of Inflammation and Ageing, QE Hospital, University of Birmingham, Birmingham B12 2TT, UK
2
Royal Orthopaedic Hospital, Bristol Road South, Birmingham B31 2AP, UK
*
Author to whom correspondence should be addressed.
Osteology 2024, 4(2), 53-63; https://doi.org/10.3390/osteology4020005
Submission received: 14 February 2024
/
Revised: 3 April 2024
/
Accepted: 17 April 2024
/
Published: 22 April 2024
(This article belongs to the Special Issue New Trends in Arthroplasty)
Abstract
:A proportion of osteoarthritis (OA) patients are unsatisfied with post-operative outcomes following total joint replacement surgery (TJR), with insufficient pain relief or poor functional improvement. Predicting those who will have poor outcomes would be beneficial for patients and clinicians. The aim of this study was to determine the relationship between baseline anthropometric data and the concentration of pre-operative serum and peri-operative synovial fluid (SF) cytokines and 7-month post-operative outcomes in a cohort of knee and hip OA patients. 160 OA patients were recruited who were scheduled for TJR. The concentration of 24 cytokines was measured in blood and SF by multiplex assay. EQ5D index health status was assessed pre-operatively and at 7 months post-operatively. 13% of patients were identified as non-responders based on EQ5D index. Compared to responders, non-responders were of higher body mass index (BMI), had greater waist and hip circumference, and had higher levels of SF leptin but lower levels of SF resistin (p < 0.05). Linear regression analysis found a significant but weak relationship between pre-operative body weight and post-operative response (ΔEQ5D index; r = 0.222, p = 0.049). The combination of body weight with SF amphiregulin and SF IL-6 provided an improved predictive model of post-operative response (r = 0.470, p = 0.035).
1. Introduction
With an aging population and an increasing incidence of obesity, the number of total joint replacements performed each year is increasing and is projected to continue to increase [1]. The main indication for the procedure is osteoarthritis (OA). In 2018, more than 95,000 hip and 100,000 knee total joint replacement surgeries were performed in England, Wales and Northern Ireland. Unfortunately, based on the quality of life patient questionnaires (EQ5D and Oxford Knee Score), a proportion of patients report dissatisfaction following joint replacement surgery [2,3,4]. Approximately 10% of hip OA patients and 20% of knee OA patients report having a poor clinical outcome, mainly characterised by a lack of pain relief or poor improvement in function [5].
In attempting to identify prognostic biomarkers of poor post-operative outcome, several studies have examined the association between serum and synovial fluid concentrations of pro-inflammatory cytokines and pain in OA patients [6,7] since it is known that such inflammatory factors are capable of promoting the sensitisation of nociceptors [8,9,10,11,12,13,14,15,16,17]. Indeed, it has previously been reported that patient reported pain is associated with distinct patterns of synovitis [18,19]. Further, specific sites of patient-reported pain in knee OA patients are associated with greater synovitis, with synovial tissue that exhibited distinct fibroblast subsets that promoted neuronal growth and survival [20]. Furthermore, high concentrations of pre-operative synovial fluid TNFα and IL-6 were associated with increased post-operative pain at two-year follow-up [21]. In a separate study, a significant correlation was reported between peak C-reactive protein (CRP) levels and the level of pain and stiffness at 2 days post-operatively [22]. However, to date, no study has performed multiplexing profiling of both peri-operative synovial fluid and pre-operative serum cytokines and analysed their associations with post-operative joint replacement outcomes in patients with knee and hip OA [23]. Therefore, the aim of this study was to investigate firstly pre-operative serum and secondly peri-operative synovial fluid cytokines as potential predictive biomarkers of joint replacement outcome in a cohort of patients with osteoarthritis.
2. Materials and Methods
2.1. Patients
Following ethical approval (UK National Research Ethics Committee 14/ES/1044), 160 OA patients were recruited to the study who were scheduled to undergo elective total hip replacement surgery (n = 97) or total knee replacement surgery (n = 63) at either the Royal Orthopaedic Hospital, Birmingham, UK, or Russells Hall Hospital, Dudley, UK. Kellgren/Lawrence grade (K/L) grading of pre-operative X-ray radiographs was performed to determine OA severity [24]. Based on radiographic joint assessments, patients who exhibited secondary causes of OA were excluded from the study. Secondary causes of OA in the hip included developmental dysplasia, avascular necrosis, Perthes disease, slipped upper femoral epiphysis, and previous acetabular or femoral neck fractures. Secondary causes of OA in the knee included malalignment due to a previous fracture of the knee, tibia or femur, a history of significant ligament injury, and avascular necrosis. Pre-operatively, anthropometric data (including body mass index (BMI), fat%, waist and hip circumference and the respective waist-to-hip ratio) were recorded. Patients completed EQ5D [25] pre-operatively and at 7 months post-operatively. Following completion of questionnaires, pre- and post-operative data were available for the EQ5D index (EQ5Di) and the five components of EQ5D, namely “mobility”, “usual activities”, “self-care”, “pain/discomfort” and “anxiety/depression”. Pre-operatively, blood samples were collected, and peri-operatively, synovial fluids were aspirated from the joint.
2.2. Quantification of Serum and Synovial Fluid Cytokines by Multiplex Bead Assay
To determine cytokine and chemokine concentrations in serum and synovial fluids, multiplex technology (Luminex® Screening Assay, R&D Systems, Abingdon, UK) was performed. In the case of synovial fluid, samples were treated with 2 mg/mL hyaluronidase as previously described [26]. Multi-plex analysis was performed according to the manufacturer’s instructions. In brief, 50 µL of a 1× antibody magnetic bead stock (Adiponectin, Serpin E1, Aggrecan, Amphiregulin, CCL11, CCL2, CCl3, CCL20, Chemerin, CXCL10, Dkk1, Galectin-1, gp130, IL1β, IL10, IL15, IL7, Visfatin, TNF-α, Galectin-3, Galectin-3BP, Lipocalin-2, CCL-4, FABP4, LIF, Leptin, IL6, Resistin and MMP-1, -2, -3, -7, -8, -9, -10, -12, -13) was added to each well of a 96-well plate. 50 µL of standard solution, serum or hyaluronidase-treated synovial fluid was then added to relevant wells and incubated for 2 h. Post-incubation, the plate was washed 3× with wash buffer, and 50 µL of a biotinylated antibody was added to all wells. Following a 1 h incubation, the plate was washed 3× and 50 µL of diluted streptavidin-PE was added to all wells. After a further 30 min incubation in the dark, the plate was washed as before and cytokine concentrations analysed using a Luminex® 200™ instrument (Luminex® Corporation, Austin, TX, USA).
2.3. Statistical Analysis
Data distribution was assessed using Kolmogorov–Smirnov test and was found to not be normally distributed. Chi square tests were used to determine differences in numbers of patients between responders and non-responders in the different categorisation levels L1–L3 for each of the EQ5D components. Mann–Whitney tests were performed to determine significance. Data are presented as medians with an interquartile range (IQR). Linear regression analysis was performed (using SPSS v24 software) to determine the relationships between baseline anthropometric, disease severity characteristics, and cytokine concentrations and the change in the pre-operative and 7-month post-operative EQ5D index (ΔEQ5D). Univariate linear regression was performed, followed by multivariate regression to account for potential confounders including joint severity (K/L grade, joint space), BMI, age, waist and hip circumference and body weight. Significance was accepted as p < 0.05.
3. Results
3.1. Identification of Patients with Poor Post-Operative Outcomes and Analysis of EQ5D Index Components
Based on the pre-operative and post-operative EQ5D index, 87% (n = 139) of patients had positive post-operative outcomes and were classified as ‘responders’. The remaining 13% (n = 21) of patients who either had a negative outcome or no improvement were classified as “non-responders” (Figure 1a,b). When examining the outcome of hip and knee joint replacement separately, 89% (n = 86) of hip OA patients had positive responses following hip replacement, and 84% (n = 53) of knee OA patients had positive responses following knee replacement (Figure 1a,b).
For the pain/discomfort EQ5D component, 72% (n = 123) of patients improved following the surgery. When examining outcomes between hip and knee joint replacement separately, 77% (n = 81) of hip OA patients had positive pain/discomfort improvement following hip replacement, and 63% (n = 42) of knee OA patients had positive pain/discomfort improvement following knee replacement (Table 1).
Analysis of individual EQ5D components showed that responders improved significantly (p < 0.001) in each of the EQ5D components, namely mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The greatest improvements were in the mobility EQ5D component, with 100% of hip OA and 100% of knee OA responder patients reporting no problem with their post-operative mobility (Table 1). Notably, the EQ5D pain component showed the least improvement post-operatively, with 15% of hip OA responders and 43% of knee OA responders still reporting “Level 2, some problem”. In contrast, those patients identified as non-responders saw no improvement in any of the EQ5D components (Table 1).
3.2. EQ5D Non-Responders Exhibited Greater Pre-Operative Adiposity and Differential Concentrations of Peri-Operative Synovial Fluid Resistin and Leptin
The baseline pre-operative characteristics, including anthropometric data, disease severity and serum/synovial fluid cytokines, were compared between responder and non-responder patients. Compared to responder patients, non-responder patients had significantly greater BMI (p < 0.05), waist circumference (p < 0.05), and hip circumference (p < 0.05), but not waist-to-hip ratio. However, there was no difference in either K/L grade or joint space between responder and non-responder patients, suggesting that OA joint grade severity at the time of surgery was not a factor in post-operative outcomes (Table 2).
In total, we determined the concentration of 24 cytokines/adipokines in peri-operative synovial fluid and pre-operative serum, which have previously been reported to be associated with the inflammatory phenotype of OA [27,28,29]. There was no significant difference in the concentration of any of the 24 cytokines in the serum between responders and non-responder patients. However, comparison of peri-operative synovial fluid cytokine concentrations revealed that non-responders had significantly lower levels of resistin (0.97 ng/mL vs. 2.96 ng/mL, p < 0.05) but significantly greater levels of leptin (54.5 ng/mL vs. 16.3 ng/mL; p < 0.05), compared to responders (Table 2).
3.3. The Relationship between Pre-Operative Anthropometric Data and Post-Operative Change in EQ5D
Performing linear regression analysis, we found that neither K/L grade nor joint space at the time of joint surgery was significantly related to ΔEQ5D. Furthermore, despite non-responders having on average a significantly higher BMI, waist circumference and hip circumference, we did not observe a significant relationship between these variables and ΔEQ5D. However, baseline body weight showed a weak but significant negative relationship to ΔEQ5D (β = −0.005, r = 0.22, p = 0.049), suggesting that high body weight at baseline explains a proportion of the poor post-operative response (Table 3).
3.4. The Relationship between Pre-Operative Serum and Synovial Fluid Cytokines and Post-Operative Change in EQ5D
Of the 24 cytokines quantified in the serum, we observed no significant relationship between cytokine concentration and ΔEQ5D in either univariate or multivariate analysis (Table 4). Similarly, regression analysis of synovial fluid cytokines revealed no significant relationship between cytokine concentration and ΔEQ5D in either univariate or multivariate analysis with confounders (Table 5). However, there was a trend for a positive relationship between synovial fluid concentrations of IL-6 and ΔEQ5D (r = 0.288, p = 0.07) in univariate analysis, and a trend for a negative relationship between amphiregulin synovial fluid concentration and ΔEQ5D in both univariate (r = 0.268, p = 0.09) and multivariate analysis (r = 0.619, p = 0.07).
Exploring this further, we examined using multiple regression whether the combination of body weight with amphiregulin and/or IL-6 peri-operative synovial fluid concentration would provide a better predictor of ΔEQ5D (Table 6). Amphiregulin synovial fluid concentration with body weight was a significant predictor for the post-operative change in EQ5D (r = 0.434, p = 0.023), with the equation ΔEQ5D = −0.108 [Amphiregulin ng/mL] – 0.0006 [Body weight kg] + 1.052. Similarly, IL-6 synovial fluid concentration with body weight was also a significant predictor for the post-operative change in EQ5D (r = 0.418, p = 0.035), with the equation ΔEQ5D = 7.4 × 10−5 [IL-6 pg/mL] – 0.006 [body weight kg] + 0.871. Finally, the combination of both amphiregulin and IL-6 synovial fluid concentrations with body weight provided a marginal improvement in the relationship (r = 0.470, p = 0.035) with the equation ΔEQ5D = 8.3 × 10−5 [IL-6 pg/mL] – 0.142 [Amphiregulin ng/mL] – 0.005 [Body weight kg] + 0.919.
4. Discussion
This is the first study to profile pre-operative and peri-operatively 24 serum and synovial fluid cytokines and to analyse their associations with post-operative outcomes in knee and hip OA patients following joint replacement surgery. Based on the change in the EQ5D index, we identified a population of knee and hip OA patients as non-responders with little to no improvement in any of the five individual EQ5D components, including mobility, self-care, usual activities, pain/discomfort and anxiety/depression.
The comparison between the responder and non-responder groups revealed that on average, the non-responders were of greater adiposity, with a higher BMI and waist and hip circumferences. In line with this observation, non-responders also had significantly higher levels of the obesity-associated adipokine leptin in their joint synovial fluid at the time of joint replacement surgery. Interestingly, we observed no difference in the concentration of leptin in the pre-operative serum between responders and non-responders, suggesting that it is the local effect of adiposity on the joint that is more relevant to post-operative outcomes. Taken together, these observations infer that the obesity phenotype is associated with poor post-operative outcomes. Notably, obesity is a known risk factor for OA [30,31,32,33], and recently it was determined via molecular endotyping that obesity impacts the inflammatory synovial fibroblast phenotype of not only load-bearing (e.g., knees and hips) [27,28] but also non-load bearing joints such as the hands [34]. Furthermore, we and others have previously reported that obesity impacts the phenotype of multiple tissues within the synovial joint, including cartilage [35], subchondral bone [36] and skeletal muscle [37,38]. This is in line with a recently published meta-analysis that demonstrated that pre-surgical obesity was associated with worse clinical outcomes of joint replacement procedures [39]. Furthermore, peri-operative levels of synovial fluid leptin were previously reported to significantly correlate with the pre-operative level of pain reported in patients with hip OA [7].
Given the association we observed between higher adiposity and leptin in non-responders, conversely, we found that synovial fluid levels of the adipokine resistin were lower in non-responders. However, in humans, there is conflicting data on the association of resistin with obesity, with some studies reporting no difference in circulatory levels of resistin between obese and non-obese individuals [40] and resistin levels not changing upon weight loss [41].
In attempting to establish a predictive model for post-operative outcomes, we performed a linear regression analysis of baseline anthropometric and cytokine profiles with a change in the EQ5D index from pre- to post-operation. Firstly, our analysis found no relationship between pre-operative K/L grade or joint space with ΔEQ5D, suggesting that disease severity at the time of surgery does not impact the likely outcome. Furthermore, despite finding that peri-operative concentrations of both resistin and leptin were different between responders and non-responders, neither were found to be significantly related to ΔEQ5D when undertaking linear regression analysis. However, our finding that body weight was significantly but negatively related to ΔEQ5D provides further support for the notion that increased adiposity is a likely predictor of poor post-operative outcomes. Despite not finding a significant relationship between ΔEQ5D and either the serum or synovial fluid concentration of any one individual cytokine, the synovial fluid concentrations of amphiregulin and/or IL-6 in combination with body weight provided a model that could explain a proportion of the ΔEQ5D response. Synovial fluid concentrations of IL-6 were on average greater in responders and were positively related to ΔEQ5D in the predictive model. A substantial body of evidence has implicated IL-6 as a pro-inflammatory mediator, and its expression within the OA joint has been related to both joint severity and OA progression. Mechanistically, IL-6 has been purported to mediate both degradative (e.g., MMPs) and anti-catabolic protective (e.g., tissue inhibitors of metalloproteinases (TIMPs)) mediators, which it has been suggested is due to classic vs. trans-signalling [42]. Our finding here on its role in predicting post-operative outcomes is therefore difficult to interpret, but it could suggest that those individuals whose local joint inflammation is predominantly mediated by aberrant IL-6 signalling are more likely to see improvements post-operatively after joint replacement surgery. Median levels of amphiregulin were not different between responders and non-responders. Nevertheless, in our predictive model, its concentration in per-operative synovial fluid was found to be negatively associated with ΔEQ5D. Amphiregulin acts via the epidermal growth factor receptor (EGFR) to induce the catabolic matrix metalloprotease 13 (MMP13) from synovial fibroblasts, a key driver of type II collagen degradation in articular cartilage [43].
Ultimately, the lack of a strong relationship between any one individual cytokine and ΔEQ5D suggests that a highly accurate and predictive model is most likely to be developed through the combination of multiple biomarkers, including anthropometric data. Furthermore, our data would suggest that the pre-operative concentration of cytokines in the joint synovial fluid will provide better predictive biomarkers than serum cytokine concentrations.
This study has some limitations. Firstly, the expansion of the panel of 24 cytokines/adipokines included in our study would provide a more comprehensive cytokine profile related to mediating pain, which could be informative. Secondly, this study represents a relatively small cohort of 160 hip and knee OA patients and therefore requires validation in a larger cohort. However, the observed difference we report in the proportion of positive outcomes between knee and hip joint replacements has been reported previously [44,45,46] and thus suggests that the dataset is representative of larger dataset studies. Finally, the absence of post-operative levels of cytokines and adipokines means that we cannot determine the impact of post-operative cytokines on outcome.
5. Conclusions
In conclusion, this study did not find any statistically significant relationship between ΔEQ5D and any individual cytokine in either pre-operative serum or peri-operative synovial fluid. However, a predictive model combining the synovial fluid concentrations of the cytokines amphiregulin and IL-6 with body weight pre-operatively could explain a proportion of the post-operative change in the EQ5D index health status. Further analysis of pre-operative markers and validation across a larger patient cohort is required in order to develop a highly accurate and predictive model that could have utility for both clinicians and their patients.
Author Contributions
Conceptualization, S.W.J. and E.T.D.; methodology, D.E.N., E.T.D. and S.W.J.; formal analysis, D.E.N. and S.W.J.; investigation, D.E.N., E.T.D. and S.W.J.; resources, S.W.J. and E.T.D.; data curation, S.W.J., D.E.N. and E.T.D.; writing—original draft preparation, D.E.N.; writing—review and editing, S.W.J.; supervision, S.W.J.; funding acquisition, S.W.J. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Medical Research Council, grant number MR/W026961/1 and Versus Arthritis, grant number 21530.
Institutional Review Board Statement
Ethics approval was provided by the UK National Research Ethics Committee (NRES 14/ES/1044), and informed consent was obtained from all patients.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Data reported in the study are available upon request to the corresponding author.
Acknowledgments
We gratefully acknowledge all the patients who agreed to take part in this study and the research nurses who helped coordinate this study at the Royal Orthopaedic Hospital and Russell Hall Hospital in Birmingham, England.
Conflicts of Interest
The authors declare funding from the Medical Research Council and Versus Arthritis. The funders had no role in the design of the study, in the collection, analysis or interpretation of data, in the writing of the manuscript or in the decision to publish the results.
References
- Matharu, G.S.; Culliford, D.J.; Blom, A.W.; Judge, A. Projections for primary hip and knee replacement surgery up to the year 2060: An analysis based on data from The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man. Ann. R Coll. Surg. Engl. 2022, 104, 443–448. [Google Scholar] [CrossRef] [PubMed]
- Verhaar, J. Patient satisfaction after total knee replacement-still a challenge. Acta Orthop. 2020, 91, 241–242. [Google Scholar] [CrossRef] [PubMed]
- Choi, Y.J.; Ra, H.J. Patient Satisfaction after Total Knee Arthroplasty. Knee Surg. Relat. Res. 2016, 28, 1–15. [Google Scholar] [CrossRef] [PubMed]
- Kim, M.S.; Koh, I.J.; Choi, Y.J.; Lee, J.Y.; In, Y. Differences in Patient-Reported Outcomes Between Unicompartmental and Total Knee Arthroplasties: A Propensity Score-Matched Analysis. J. Arthroplast. 2017, 32, 1453–1459. [Google Scholar] [CrossRef] [PubMed]
- Kahlenberg, C.A.; Nwachukwu, B.U.; McLawhorn, A.S.; Cross, M.B.; Cornell, C.N.; Padgett, D.E. Patient Satisfaction After Total Knee Replacement: A Systematic Review. HSS J. 2018, 14, 192–201. [Google Scholar] [CrossRef] [PubMed]
- Thudium, C.S.; Lofvall, H.; Karsdal, M.A.; Bay-Jensen, A.C.; Bihlet, A.R. Protein biomarkers associated with pain mechanisms in osteoarthritis. J. Proteom. 2019, 190, 55–66. [Google Scholar] [CrossRef] [PubMed]
- Bas, S.; Finckh, A.; Puskas, G.J.; Suva, D.; Hoffmeyer, P.; Gabay, C.; Lubbeke, A. Adipokines correlate with pain in lower limb osteoarthritis: Different associations in hip and knee. Int. Orthop. 2014, 38, 2577–2583. [Google Scholar] [CrossRef] [PubMed]
- Miller, R.E.; Miller, R.J.; Malfait, A.M. Osteoarthritis joint pain: The cytokine connection. Cytokine 2014, 70, 185–193. [Google Scholar] [CrossRef]
- Orita, S.; Koshi, T.; Mitsuka, T.; Miyagi, M.; Inoue, G.; Arai, G.; Ishikawa, T.; Hanaoka, E.; Yamashita, K.; Yamashita, M.; et al. Associations between proinflammatory cytokines in the synovial fluid and radiographic grading and pain-related scores in 47 consecutive patients with osteoarthritis of the knee. BMC Musculoskelet. Disord. 2011, 12, 144. [Google Scholar] [CrossRef] [PubMed]
- Schafers, M.; Lee, D.H.; Brors, D.; Yaksh, T.L.; Sorkin, L.S. Increased sensitivity of injured and adjacent uninjured rat primary sensory neurons to exogenous tumor necrosis factor-alpha after spinal nerve ligation. J. Neurosci. 2003, 23, 3028–3038. [Google Scholar] [CrossRef] [PubMed]
- Richter, F.; Natura, G.; Loser, S.; Schmidt, K.; Viisanen, H.; Schaible, H.G. Tumor necrosis factor causes persistent sensitization of joint nociceptors to mechanical stimuli in rats. Arthritis Rheum. 2010, 62, 3806–3814. [Google Scholar] [CrossRef] [PubMed]
- Qin, X.; Wan, Y.; Wang, X. CCL2 and CXCL1 trigger calcitonin gene-related peptide release by exciting primary nociceptive neurons. J. Neurosci. Res. 2005, 82, 51–62. [Google Scholar] [CrossRef] [PubMed]
- Kao, D.J.; Li, A.H.; Chen, J.C.; Luo, R.S.; Chen, Y.L.; Lu, J.C.; Wang, H.L. CC chemokine ligand 2 upregulates the current density and expression of TRPV1 channels and Nav1.8 sodium channels in dorsal root ganglion neurons. J. Neuroinflamm. 2012, 9, 189. [Google Scholar] [CrossRef] [PubMed]
- Opree, A.; Kress, M. Involvement of the proinflammatory cytokines tumor necrosis factor-alpha, IL-1 beta, and IL-6 but not IL-8 in the development of heat hyperalgesia: Effects on heat-evoked calcitonin gene-related peptide release from rat skin. J. Neurosci. 2000, 20, 6289–6293. [Google Scholar] [CrossRef] [PubMed]
- Stemkowski, P.L.; Smith, P.A. Long-term IL-1beta exposure causes subpopulation-dependent alterations in rat dorsal root ganglion neuron excitability. J. Neurophysiol. 2012, 107, 1586–1597. [Google Scholar] [CrossRef] [PubMed]
- Brenn, D.; Richter, F.; Schaible, H.G. Sensitization of unmyelinated sensory fibers of the joint nerve to mechanical stimuli by interleukin-6 in the rat: An inflammatory mechanism of joint pain. Arthritis Rheum. 2007, 56, 351–359. [Google Scholar] [CrossRef] [PubMed]
- Obreja, O.; Biasio, W.; Andratsch, M.; Lips, K.S.; Rathee, P.K.; Ludwig, A.; Rose-John, S.; Kress, M. Fast modulation of heat-activated ionic current by proinflammatory interleukin 6 in rat sensory neurons. Brain 2005, 128 Pt 7, 1634–1641. [Google Scholar] [CrossRef] [PubMed]
- de Lange-Brokaar, B.J.; Ioan-Facsinay, A.; Yusuf, E.; Visser, A.W.; Kroon, H.M.; van Osch, G.J.; Zuurmond, A.M.; Stojanovic-Susulic, V.; Bloem, J.L.; Nelissen, R.G.; et al. Association of pain in knee osteoarthritis with distinct patterns of synovitis. Arthritis Rheumatol. 2015, 67, 733–740. [Google Scholar] [CrossRef] [PubMed]
- Guermazi, A.; Roemer, F.W.; Hayashi, D.; Crema, M.D.; Niu, J.; Zhang, Y.; Marra, M.D.; Katur, A.; Lynch, J.A.; El-Khoury, G.Y.; et al. Assessment of synovitis with contrast-enhanced MRI using a whole-joint semiquantitative scoring system in people with, or at high risk of, knee osteoarthritis: The MOST study. Ann. Rheum. Dis. 2011, 70, 805–811. [Google Scholar] [CrossRef] [PubMed]
- Nanus, D.E.; Badoume, A.; Wijesinghe, S.N.; Halsey, A.M.; Hurley, P.; Ahmed, Z.; Botchu, R.; Davis, E.T.; Lindsay, M.A.; Jones, S.W. Synovial tissue from sites of joint pain in knee osteoarthritis patients exhibits a differential phenotype with distinct fibroblast subsets. EBioMedicine 2021, 72, 103618. [Google Scholar] [CrossRef]
- Gandhi, R.; Santone, D.; Takahashi, M.; Dessouki, O.; Mahomed, N.N. Inflammatory predictors of ongoing pain 2 years following knee replacement surgery. Knee 2013, 20, 316–318. [Google Scholar] [CrossRef] [PubMed]
- Hall, G.M.; Peerbhoy, D.; Shenkin, A.; Parker, C.J.; Salmon, P. Hip and knee arthroplasty: A comparison and the endocrine, metabolic and inflammatory responses. Clin. Sci. 2000, 98, 71–79. [Google Scholar] [CrossRef]
- Fernandez-de-Las-Penas, C.; Florencio, L.L.; de-la-Llave-Rincon, A.I.; Ortega-Santiago, R.; Cigaran-Mendez, M.; Fuensalida-Novo, S.; Plaza-Manzano, G.; Arendt-Nielsen, L.; Valera-Calero, J.A.; Navarro-Santana, M.J. Prognostic Factors for Postoperative Chronic Pain after Knee or Hip Replacement in Patients with Knee or Hip Osteoarthritis: An Umbrella Review. J. Clin. Med. 2023, 12, 6624. [Google Scholar] [CrossRef] [PubMed]
- Kellgren, J.H.; Lawrence, J.S. Radiological assessment of osteo-arthrosis. Ann. Rheum. Dis. 1957, 16, 494–502. [Google Scholar] [CrossRef] [PubMed]
- Wailoo, A.; Hernandez Alava, M.; Escobar Martinez, A. Modelling the relationship between the WOMAC Osteoarthritis Index and EQ-5D. Health Qual. Life Outcomes 2014, 12, 37. [Google Scholar] [CrossRef] [PubMed]
- Jayadev, C.; Rout, R.; Price, A.; Hulley, P.; Mahoney, D. Hyaluronidase treatment of synovial fluid to improve assay precision for biomarker research using multiplex immunoassay platforms. J. Immunol. Methods 2012, 386, 22–30. [Google Scholar] [CrossRef] [PubMed]
- Nanus, D.E.; Wijesinghe, S.N.; Pearson, M.J.; Hadjicharalambous, M.R.; Rosser, A.; Davis, E.T.; Lindsay, M.A.; Jones, S.W. Regulation of the Inflammatory Synovial Fibroblast Phenotype by Metastasis-Associated Lung Adenocarcinoma Transcript 1 Long Noncoding RNA in Obese Patients With Osteoarthritis. Arthritis Rheumatol. 2020, 72, 609–619. [Google Scholar] [CrossRef] [PubMed]
- Pearson, M.J.; Herndler-Brandstetter, D.; Tariq, M.A.; Nicholson, T.A.; Philp, A.M.; Smith, H.L.; Davis, E.T.; Jones, S.W.; Lord, J.M. IL-6 secretion in osteoarthritis patients is mediated by chondrocyte-synovial fibroblast cross-talk and is enhanced by obesity. Sci. Rep. 2017, 7, 3451. [Google Scholar] [CrossRef] [PubMed]
- Sohn, D.H.; Sokolove, J.; Sharpe, O.; Erhart, J.C.; Chandra, P.E.; Lahey, L.J.; Lindstrom, T.M.; Hwang, I.; Boyer, K.A.; Andriacchi, T.P.; et al. Plasma proteins present in osteoarthritic synovial fluid can stimulate cytokine production via Toll-like receptor 4. Arthritis Res. Ther. 2012, 14, R7. [Google Scholar] [CrossRef] [PubMed]
- Cicuttini, F.M.; Baker, J.R.; Spector, T.D. The association of obesity with osteoarthritis of the hand and knee in women: A twin study. J. Rheumatol. 1996, 23, 1221–1226. [Google Scholar] [PubMed]
- Holliday, K.L.; McWilliams, D.F.; Maciewicz, R.A.; Muir, K.R.; Zhang, W.; Doherty, M. Lifetime body mass index, other anthropometric measures of obesity and risk of knee or hip osteoarthritis in the GOAL case-control study. Osteoarthr. Cartil. 2011, 19, 37–43. [Google Scholar] [CrossRef] [PubMed]
- Reyes, C.; Leyland, K.M.; Peat, G.; Cooper, C.; Arden, N.K.; Prieto-Alhambra, D. Association Between Overweight and Obesity and Risk of Clinically Diagnosed Knee, Hip, and Hand Osteoarthritis: A Population-Based Cohort Study. Arthritis Rheumatol. 2016, 68, 1869–1875. [Google Scholar] [CrossRef] [PubMed]
- Park, D.; Park, Y.M.; Ko, S.H.; Hyun, K.S.; Choi, Y.H.; Min, D.U.; Han, K.; Koh, H.S. Association of general and central obesity, and their changes with risk of knee osteoarthritis: A nationwide population-based cohort study. Sci. Rep. 2023, 13, 3796. [Google Scholar] [CrossRef] [PubMed]
- Wijesinghe, S.N.; Badoume, A.; Nanus, D.E.; Sharma-Oates, A.; Farah, H.; Certo, M.; Alnajjar, F.; Davis, E.T.; Mauro, C.; Lindsay, M.A.; et al. Obesity defined molecular endotypes in the synovium of patients with osteoarthritis provides a rationale for therapeutic targeting of fibroblast subsets. Clin. Transl. Med. 2023, 13, e1232. [Google Scholar] [CrossRef] [PubMed]
- Philp, A.M.; Butterworth, S.; Davis, E.T.; Jones, S.W. eNAMPT Is Localised to Areas of Cartilage Damage in Patients with Hip Osteoarthritis and Promotes Cartilage Catabolism and Inflammation. Int. J. Mol. Sci. 2021, 22, 6719. [Google Scholar] [CrossRef] [PubMed]
- Philp, A.M.; Collier, R.L.; Grover, L.M.; Davis, E.T.; Jones, S.W. Resistin promotes the abnormal Type I collagen phenotype of subchondral bone in obese patients with end stage hip osteoarthritis. Sci. Rep. 2017, 7, 4042. [Google Scholar] [CrossRef] [PubMed]
- Nicholson, T.; Church, C.; Tsintzas, K.; Jones, R.; Breen, L.; Davis, E.T.; Baker, D.J.; Jones, S.W. Vaspin promotes insulin sensitivity of elderly muscle and is upregulated in obesity. J. Endocrinol. 2019, 241, 31–43. [Google Scholar] [CrossRef] [PubMed]
- Wilhelmsen, A.; Tsintzas, K.; Jones, S.W. Recent advances and future avenues in understanding the role of adipose tissue cross talk in mediating skeletal muscle mass and function with ageing. Geroscience 2021, 43, 85–110. [Google Scholar] [CrossRef] [PubMed]
- Pozzobon, D.; Ferreira, P.H.; Blyth, F.M.; Machado, G.C.; Ferreira, M.L. Can obesity and physical activity predict outcomes of elective knee or hip surgery due to osteoarthritis? A meta-analysis of cohort studies. BMJ Open 2018, 8, e017689. [Google Scholar] [CrossRef]
- Silha, J.V.; Krsek, M.; Skrha, J.V.; Sucharda, P.; Nyomba, B.L.; Murphy, L.J. Plasma resistin, adiponectin and leptin levels in lean and obese subjects: Correlations with insulin resistance. Eur. J. Endocrinol. 2003, 149, 331–335. [Google Scholar] [CrossRef]
- Lee, J.H.; Chan, J.L.; Yiannakouris, N.; Kontogianni, M.; Estrada, E.; Seip, R.; Orlova, C.; Mantzoros, C.S. Circulating resistin levels are not associated with obesity or insulin resistance in humans and are not regulated by fasting or leptin administration: Cross-sectional and interventional studies in normal, insulin-resistant, and diabetic subjects. J. Clin. Endocrinol. Metab. 2003, 88, 4848–4856. [Google Scholar] [CrossRef] [PubMed]
- Wiegertjes, R.; van de Loo, F.A.J.; Blaney Davidson, E.N. A roadmap to target interleukin-6 in osteoarthritis. Rheumatology 2020, 59, 2681–2694. [Google Scholar] [CrossRef]
- Chen, Y.T.; Hou, C.H.; Hou, S.M.; Liu, J.F. The effects of amphiregulin induced MMP-13 production in human osteoarthritis synovial fibroblast. Mediat. Inflamm. 2014, 2014, 759028. [Google Scholar] [CrossRef] [PubMed]
- Bachmeier, C.J.; March, L.M.; Cross, M.J.; Lapsley, H.M.; Tribe, K.L.; Courtenay, B.G.; Brooks, P.M.; Arthritis, C.; Outcome Project, G. A comparison of outcomes in osteoarthritis patients undergoing total hip and knee replacement surgery. Osteoarthr. Cartil. 2001, 9, 137–146. [Google Scholar] [CrossRef] [PubMed]
- O‘Brien, S.; Bennett, D.; Doran, E.; Beverland, D.E. Comparison of hip and knee arthroplasty outcomes at early and intermediate follow-up. Orthopedics 2009, 32, 168. [Google Scholar] [PubMed]
- Choi, J.K.; Geller, J.A.; Yoon, R.S.; Wang, W.; Macaulay, W. Comparison of total hip and knee arthroplasty cohorts and short-term outcomes from a single-center joint registry. J. Arthroplast. 2012, 27, 837–841. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Responders and non-responders defined by change in the pre-operative and 7-month post-operative EQ5D index. (a) responder patients, (b) non-responder patients. *** = significantly different between pre- and post-joint replacement surgery, p < 0.001.
Figure 1.
Responders and non-responders defined by change in the pre-operative and 7-month post-operative EQ5D index. (a) responder patients, (b) non-responder patients. *** = significantly different between pre- and post-joint replacement surgery, p < 0.001.
Table 1.
Pre- and post-operative EQ5D index. The EQ5D index was assessed against five different components: mobility, self-care, usual activities, pain/discomfort and anxiety/depression, with each component scored as either L1 (no problem), L2 (some problem) or L3 (severe problem). Data is represented as % and number of patients (% (n)) for each component and each level of EQ5D. Dark green, mid-dark and light green-coloured cells represent >75%, >50% and greater than 25% improvement between pre- and post-operative patient numbers. The Chi square test compared the post-operative % of patients at each level for each of the five EQ5D components, with ‡ representing p < 0.001 significantly different between responders and non-responders.
Table 1.
Pre- and post-operative EQ5D index. The EQ5D index was assessed against five different components: mobility, self-care, usual activities, pain/discomfort and anxiety/depression, with each component scored as either L1 (no problem), L2 (some problem) or L3 (severe problem). Data is represented as % and number of patients (% (n)) for each component and each level of EQ5D. Dark green, mid-dark and light green-coloured cells represent >75%, >50% and greater than 25% improvement between pre- and post-operative patient numbers. The Chi square test compared the post-operative % of patients at each level for each of the five EQ5D components, with ‡ representing p < 0.001 significantly different between responders and non-responders.
| Non-Responders | Mobility | Self-Care | Usual Activities | Pain/Discomfort | Anxiety/Depression | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| All | Pre | Post | Pre | Post | Pre | Post | Pre | Post | Pre | Post |
| L1 | 19%(14) | 18%(13) | 80%(91) | 75%(86) | 17%(12) | 13%(9) | 4%(2) | 4%(2) | 80%(95) | 77%(92) |
| L2 | 81%(60) | 82%(61) | 20%(23) | 24%(27) | 80%(57) | 83%(59) | 89%(41) | 89%(41) | 19%(23) | 22%(26) |
| L3 | 0%(0) | 0%(0) | 0%(0) | 1%(1) | 3%(2) | 4%(3) | 7(3) | 7(3) | 1%(1) | 1%(1) |
| Hips | Pre | Post | Pre | Post | Pre | Post | Pre | Post | Pre | Post |
| L1 | 23%(9) | 23%(9) | 75%(45) | 70%(42) | 23%(9) | 21%(8) | 10%(2) | 10%(2) | 79%(54) | 79%(53) |
| L2 | 78%(31) | 78%(31) | 25%(15) | 30%(18) | 74%(29) | 74%(29) | 76%(16) | 76%(16) | 19%(13) | 21%(14) |
| L3 | 0%(0) | 0%(0) | 0%(0) | 0%(0) | 3%(1) | 5%(2) | 14%(3) | 14%(3) | 2%(1) | 2%(1) |
| Knees | Pre | Post | Pre | Post | Pre | Post | Pre | Post | Pre | Post |
| L1 | 15%(5) | 12%(4) | 85%(46) | 82%(44) | 9%(3) | 3%(1) | 0%(0) | 0%(0) | 80%(41) | 77%(39) |
| L2 | 85%(29) | 88%(30) | 15%(8) | 17%(9) | 88%(28) | 94%(30) | 100%(25) | 100%(25) | 20%(10) | 24%(12) |
| L3 | 0%(0) | 0%(0) | 0%(0) | 2%(1) | 3%(1) | 3%(1) | 0%(0) | 0%(0) | 0%(0) | 0%(0) |
| Responders | Mobility | Self-Care | Usual Activities | Pain/Discomfort | Anxiety/Depression | |||||
| All | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ |
| L1 | 0%(0) | 100%(91) | 0%(0) | 96%(48) | 0%(0) | 90%(86) | 0%(0) | 76%(93) | 0%(0) | 92%(44) |
| L2 | 100%(91) | 0%(0) | 96%(48) | 4%(2) | 87%(83) | 10%(100 | 54%(66) | 24%(30) | 88(42) | 8%(4) |
| L3 | 0%(0) | 0%(0) | 4%(2) | 0%(0) | 14%(13) | 0%(0) | 46%(57) | 0%(0) | 13%(6) | 0%(0) |
| Hips | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ |
| L1 | 0%(0) | 100%(59) | 0%(0) | 95%(37) | 0%(0) | 89%(55) | 0%(0) | 85%(69) | 0%(0) | 91%(30) |
| L2 | 100%(59) | 0%(0) | 95%(37) | 5%(2) | 86%(53) | 11%(7) | 61%(49) | 15%(12) | 85%(28) | 9%(3) |
| L3 | 0%(0) | 0%(0) | 5%(2) | 0%(0) | 15%(9) | 0%(0) | 40%(32) | 0%(0) | 15%(5) | 0%(0) |
| Knees | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ | Pre | Post ‡ |
| L1 | 0%(0) | 100%(32) | 0%(0) | 100%(11) | 0%(0) | 91%(31) | 0%(0) | 57%(24) | 0%(0) | 93%(14) |
| L2 | 100%(32) | 0%(0) | 100%(11) | 0%(0) | 88%(30) | 9%(3) | 41%(17) | 43%(18) | 93%(14) | 7%(1) |
| L3 | 0%(0) | 0%(0) | 0%(0) | 0%(0) | 12%(4) | 0%(0) | 60%(25) | 0%(0) | 7%(1) | 0%(0) |
Table 2.
Baseline patient characteristics between EQ5D index responders and non-responders. All values are shown as medians with IQR. * = significantly different between responders and non-responders, p < 0.05. Amph = amphiregulin; Adpn = adiponectin.
Table 2.
Baseline patient characteristics between EQ5D index responders and non-responders. All values are shown as medians with IQR. * = significantly different between responders and non-responders, p < 0.05. Amph = amphiregulin; Adpn = adiponectin.
| All Patients | Responders | Non-Responders | ||||
|---|---|---|---|---|---|---|
| Age (years) | 70 (62,75) | 70 (62, 75) | 72.5 (63.5, 76.5) | |||
| Height (cm) | 166 (160, 175) | 167 (160, 175) | 163 (160, 173) | |||
| Weight (kg) | 75.7 (67.4, 91.7) | 75.3 (66, 89.9) | 81.4 (73, 93.7) | |||
| BMI (kg/m2) | 27.3 (24.8, 30.7) | 26.8 (24.6, 30,4) * | 29.9 (27.5, 33.6) * | |||
| WC (cm) | 96.3 (85.3, 107) | 96 (84, 105) * | 108 (91, 112) * | |||
| HC (cm) | 107 (99, 113) | 106 (99, 112) * | 113 (104, 121) * | |||
| WHR | 0.91 (0.84, 0.96) | 0.91 (0.84, 0.95) | 0.92 (0.88, 0.98) | |||
| Joint Space (mm) | 0 (0, 1) | 0 (0, 1) | 0 (0, 3.3) | |||
| K/L Grade | 4 (3, 4) | 4 (3, 4) | 4 (2.8, 4) | |||
| Serum | Synovial Fluid | Serum | Synovial Fluid | Serum | Synovial Fluid | |
| TNFα (pg/mL) | 5.1 (4.2, 5.6) | 5.1 (2.8, 9.5) | 5.1 (4.2, 5.6) | 5.1 (2.8, 9.5) | 5.2 (4, 5.6) | 5.1 (1.4, 14.0) |
| Visfatin (ng/mL) | 2.6 (1.8, 3.3) | 0 (0, 32) | 2.6 (1.8, 3.3) | 0 (0, 36) | 2.6 (1.8, 3.3) | 0 (0, 0) |
| IL10 (pg/mL) | 4.81 (4.46, 4.99) | 17.3 (12.2, 22.6) | 4.8 (4.46,4.99) | 17.3 (11.5, 21.6) | 4.7 (4.46, 5.05) | 21.6 (16.1, 31.3) |
| IL1B (pg/mL) | 15.9 (13.7, 17.8) | 25.1 (9.6, 31.6) | 15.9 (13.3, 17.8) | 28.4 (9.0, 31.6) | 16.1 (14.5, 18.2) | 16.3 (7.2, 36.3) |
| DKK1 (ng/mL) | 3.3 (2.3, 4.7) | 0.50 (0.35, 0.71) | 3.3 (2.3, 4.7) | 0.48 (0.35, 0.72) | 3.6 (2.2, 4.7) | 0.53 (0.41, 0.84) |
| MIP1α (ng/mL) | 0.42 (0.03, 0.54) | 0.34 (0.24, 0.39) | 0.42 (0.03, 0.53) | 0.34 (0.24, 0.39) | 0.46 (0.02, 0.60) | 0.43 (0.16, 0.46) |
| gal1 (ng/mL) | 43.9 (31.1, 58.0) | 110 (87, 126) | 40.7 (29.9, 57.7) | 109 (86, 127) | 53.4 (43.2, 67.8) | 111 (90, 125) |
| Chemerin (ng/mL) | 5.6 (3.4, 7.9) | 3.0 (2.4, 3.6) | 5.6 (3.5, 8.0) | 3.0 (2.4, 3.6) | 4.8 (2.7, 6.8) | 3.3 (3.1, 4.0) |
| Eotaxin (pg/mL) | 125 (61, 228) | 28.9 (9.5, 42.6) | 125 (62, 228) | 28.9 (9.5, 42.6) | 82 (61, 214) | 28.9 (9.5, 42.6) |
| gp130 (ng/mL) | 93 (69, 105) | 71.4 (61.6 76.8) | 93 (69, 104) | 71.4 (61.5, 77.2) | 102 (56, 108) | 67.6 (58.9, 76.8) |
| ip10 (pg/mL) | 23.4 (18.4, 32.4) | 104.4 (70.7, 151.4 | 23.9 (18.4, 36.7) | 106.3 (70.4, 152.9) | 22.8 (17.4, 26.6) | 98.9 (73.2, 265.1) |
| MCP1 (ng/mL) | 0.35 (0.25, 0.46) | 0.30 (0.15, 0.61) | 0.35 (0.25, 0.47) | 0.29 (0.15, 0.64) | 0.35 (0.27, 0.41) | 0.30 (0.18, 0.42) |
| IL7 (pg/mL) | 2.7 (1.8, 3.9) | 4.0 (2.8, 5.2) | 2.7 (1.8, 3.4) | 4.0 (2.8, 5.2) | 2.9 (2.2, 4.4) | 4.6 (4.0, 6.3) |
| MIP3α (pg/mL) | 34.4 (8.8, 59.9) | 22.7 (13.1, 42.6) | 33.5 (8.8, 54.7) | 22.9 (13.8, 43.7) | 62.8 (7.1, 71.2) | 9.4 (7.3, 42.1) |
| Amph (ng/mL) | 0.59 (0.59, 0.59) | 1.36 (0.55, 1.36) | 0.59 (0.48, 0.59) | 1.36 (0.55, 1.36) | 0.59 (0.59, 0.59) | 1.36 (0.78, 1.65) |
| IL15 (pg/mL) | 4.0 (3.0, 4.7) | 31.1 (20.9, 35.2) | 4.0 (3.0, 4.9) | 31.1 (18.8, 34.9) | 3.4 (3.1, 3.8) | 34.9 (27.0, 37.3) |
| Aggrecan (pg/mL) | 163 (116, 217) | 0 (0, 114) | 163 (116, 217) | 0 (0, 114) | 116 (116, 204) | 0 (0, 244) |
| Resistin (ng/mL) | 14.5 (11.2, 18.3) | 2.90 (1.72, 5.19) | 14.5 (11.2, 19.0) | 2.96 (1.99, 5.70) | 14.9 (8.7, 16.2) | 0.97 (0.53, 2.65)* |
| Serpin E1 (ng/mL) | 135 (100, 167) | 15.6 (8.5, 42.7) | 137 (105, 167) | 16.2 (8.4, 49.4) | 113 (19, 165) | 11.0 (6.6, 18.3) |
| Adpn (µg/mL) | 9.9 (6.34, 13.5) | 2.7 (2.0, 4.6) | 9.9 (6.8, 14.2) | 2.8 (2.0, 5.3) | 8.5 (1.4, 11.5) | 2.5 (1.6, 3.2) |
| IL6 (pg/mL) | 2.1 (0.0, 2.7) | 132 (57, 453) | 2.1 (0, 2.8) | 175 (64, 577) | 0 (0, 2.0) | 69 (8, 222) |
| Leptin (ng/mL) | 13.1 (6.7, 26.1) | 17.3 (7.1, 54.6) | 12.6 (6.3, 26.1) | 16.3 (6.5, 48.1) | 23.3 (12.7, 27.6) | 54.5 (40.3, 67.9) * |
| FABP4 (ng/mL) | 18.0 (12.6, 28.2) | 16.7 (8.2, 81.4) | 17.9 (11.8, 27.2) | 16.1 (7.7, 66.0) | 18.8 (15.6, 41.0) | 60.1 (11.7, 159.6) |
| MIP1β (ng/mL) | 152 (105, 184) | 55.9 (0, 105.3) | 150 (101, 187) | 55.9 (0, 105.3) | 128 (85, 165) | 0 (0, 80.6) |
Table 3.
The relationship between anthropometric data and ΔEQ5D (pre- and 7-month post-operative) *.
Table 3.
The relationship between anthropometric data and ΔEQ5D (pre- and 7-month post-operative) *.
| Linear REGRESSION Coefficient (95% CI) † | r | p-Value | |
|---|---|---|---|
| AGE | −0.003 (−0.012, 0.006) | 0.065 | 0.564 |
| Height (cm) | −0.005 (−0.013, 0.003) | 0.137 | 0.228 |
| Weight (kg) | −0.005 (−0.009, −0.00002) | 0.222 | 0.049 |
| BMI | −0.012 (−0.026, 0.003) | 0.181 | 0.108 |
| Waist circumference (cm) | −0.003 (−0.007, 0.0007) | 0.193 | 0.104 |
| Hip circumference (cm) | −0.003 (−0.007, 0.002) | 0.145 | 0.225 |
| WHR | −0.456 (−1.35, 0.440) | 0.121 | 0.313 |
| Joint Space (mm) | −0.023 (−0.075, 0.029) | 0.103 | 0.376 |
| K and L grade | 0.015 (−0.077, 0.106) | 0.037 | 0.751 |
* 95% CI = 95% confidence interval. † Change in EQ5D per unit increase in parameter.
Table 4.
Relationship between pre-operative serum cytokines and ΔEQ5D (pre- and 7-month post-operative) *.
Table 4.
Relationship between pre-operative serum cytokines and ΔEQ5D (pre- and 7-month post-operative) *.
| Linear Regression Coefficient (95% CI) † | r | p-Value | Multiple Linear Regression Coefficient (95% CI) ‡ | r | p-Value | |
|---|---|---|---|---|---|---|
| TNF-α | −0.006 (−0.04, 0.028) | 0.046 | 0.715 | −0.004 (−0.054, 0.046) | 0.293 | 0.872 |
| Visfatin | 0.001 (−0.006, 0.07) | 0.023 | 0.856 | 0.001 (−0.007, 0.008) | 0.291 | 0.836 |
| IL-10 | −0.0003 (−0.005, 0.005) | 0.013 | 0.92 | −0.001 (−0.006, 0.005) | 0.291 | 0.824 |
| IL-1β | 0.002 (−0.001, 0.006) | 0.174 | 0.165 | 0.003 (−0.001, 0.007) | 0.346 | 0.175 |
| DKK1 | −0.024 (−0.065, 0.017) | 0.147 | 0.243 | −0.031 (−0.082, 0.021) | 0.344 | 0.239 |
| MIP1α | 0.042 (−0.223, 0.306) | 0.04 | 0.754 | 0.014 (−0.307, 0.336) | 0.292 | 0.928 |
| Galectin1 | −0.0008 (−0.003, 0.001) | 0.097 | 0.442 | −0.0001 (−0.003, 0.002) | 0.292 | 0.932 |
| Chemerin | −0.006 (−0.026, 0.013) | 0.079 | 0.531 | −0.007 (−0.032, 0.018) | 0.301 | 0.586 |
| Eotaxin | 0.00014 (−0.00028, 0.00055) | 0.082 | 0.519 | 0.00013 (−0.001, 0.001) | 0.284 | 0.968 |
| gp130 | −0.0014 (−0.004, 0.001) | 0.170 | 0.175 | −0.002 (−0.004, 0.001) | 0.336 | 0.230 |
| IP10 | 0.00046 (−0.003, 0.004) | 0.034 | 0.786 | 0.001 (−0.003, 0.005) | 0.297 | 0.679 |
| MCP1 | 0.004 (−0.059, 0.067) | 0.017 | 0.895 | 0.028 (−0.049, 0.104) | 0.308 | 0.472 |
| IL-7 | −0.018 (−0.076, 0.04) | 0.079 | 0.531 | −0.007 (−0.092, 0.078) | 0.293 | 0.872 |
| MIP3α | 6.8 × 10−6 (−0.00013, 0.00014) | 0.013 | 0.919 | 6.02 × 10−6 (−0.00016, 0.00014) | 0.292 | 0.936 |
| Amphiregulin | 0.091 (−0.340, 0.522) | 0.054 | 0.674 | 0.240 (−0.301, 0.780) | 0.315 | 0.377 |
| IL-15 | 0.002 (−0.012, 0.016) | 0.034 | 0.788 | 0.001 (−0.016, 0.018) | 0.290 | 0.920 |
| Aggrecan | 7.5 × 10−5 (−0.00014, 0.00029) | 0.088 | 0.487 | 0.0004 (−0.0001, 0.001) | 0.365 | 0.106 |
| Resistin | 0.007 (−0.001, 0.016) | 0.219 | 0.085 | 0.006 (−0.005, 0.016) | 0.329 | 0.288 |
| Serpin E1 | 0.0005 (−0.0006, 0.0016) | 0.110 | 0.383 | 0.001 (−0.001, 0.002) | 0.318 | 0.361 |
| Adiponectin | 0.008 (−0.004, 0.020) | 0.165 | 0.193 | 0.006 (−0.01, 0.022) | 0.310 | 0.467 |
| IL-6 | 1.9 × 10−5 (−0.001, 0.009) | 0.005 | 0.970 | −8.3 × 10−5 (−0.001, 0.001) | 0.293 | 0.881 |
| Leptin | 0.00019 (−0.004, 0.004) | 0.012 | 0.925 | 0.00014 (−0.007, 0.007) | 0.279 | 0.967 |
| FABP4 | 0.001 (−0.001, 0.004) | 0.145 | 0.252 | 0.001 (−0.002, 0.004) | 0.308 | 0.494 |
| MIP1β | 0.0006 (−0.0004, 0.0016) | 0.209 | 0.102 | 0.001 (−0.001, 0.002) | 0.312 | 0.430 |
* 95% CI = 95% confidence interval. † Change in EQ5D per unit increase in cytokine. ‡ Change in EQ5D per unit increase in cytokines, including age, body mass index, Kellgren/Lawrence grade, joint space, waist and hip circumference, waist-to-hip ratio and body weight in the regression equation.
Table 5.
Relationship between pre-operative synovial fluid cytokines and ΔEQ5D (pre- and 7-month post-operative) *.
Table 5.
Relationship between pre-operative synovial fluid cytokines and ΔEQ5D (pre- and 7-month post-operative) *.
| Linear Regression Coefficient (95% CI) † | r | p-Value | Multiple Linear Regression Coefficient (95% CI) ‡ | r | p-Value | |
|---|---|---|---|---|---|---|
| TNF-α | 0.001 (−0.018, 0.020) | 0.016 | 0.921 | 0.008 (−0.022, 0.037) | 0.544 | 0.584 |
| Visfatin | 0.001 (−0.001, 0.003) | 0.195 | 0.222 | 0.001 (−0.001, 0.004) | 0.560 | 0.352 |
| IL-10 | −0.0004 (−0.010, 0.009) | 0.012 | 0.940 | −0.001 (−0.016, 0.013) | 0.537 | 0.843 |
| IL-1β | 0.0005 (−0.006, 0.007) | 0.024 | 0.881 | 0.002 (−0.006, 0.011) | 0.544 | 0.589 |
| DKK1 | −0.073 (−0.225, 0.08) | 0.153 | 0.339 | −0.120 (−0.543, 0.302) | 0.545 | 0.562 |
| MIP1α | −0.284 (−0.882, 0.313) | 0.152 | 0.342 | −0.633 (−0.155, 0.283) | 0.587 | 0.166 |
| Galectin1 | 0.0018 (−0.0019, 0.0056) | 0.156 | 0.329 | 0.0003 (−0.006, 0.005) | 0.536 | 0.906 |
| Chemerin | −0.023 (−0.099, 0.052) | 0.100 | 0.534 | 0.004 (−0.117, 0.126) | 0.536 | 0.940 |
| Eotaxin | −0.0007 (−0.002, 0.001) | 0.146 | 0.362 | −0.001 (−0.002, 0.001) | 0.545 | 0.559 |
| gp130 | −0.001 (−0.008, 0.006) | 0.054 | 0.738 | −0.003 (−0.013, 0.007) | 0.545 | 0.565 |
| IP10 | 0.00013 (−0.0005, 0.0007) | 0.069 | 0.670 | 0.000019 (−0.001, 0.001) | 0.535 | 0.960 |
| MCP1 | 0.026 (−0.052, 0.105) | 0.109 | 0.497 | 0.06 (−0.03, 0.155) | 0.582 | 0.193 |
| IL-7 | −0.033 (−0.093, 0.028) | 0.172 | 0.283 | −0.031 (−0.112, 0.05) | 0.533 | 0.438 |
| MIP3α | 0.002 (−0.002, 0.006) | 0.164 | 0.307 | 0.001 (−0.005, 0.007) | 0.541 | 0.654 |
| Amphiregulin | −0.158 (−0.343, 0.026) | 0.268 | 0.09 | −0.24 (−0.5, 0.021) | 0.619 | 0.07 |
| IL-15 | −0.001 (−0.009, 0.008) | 0.020 | 0.90 | −0.003 (−0.016, 0.010) | 0.543 | 0.615 |
| Aggrecan | −0.0005 (−0.0015, 0.0005) | 0.169 | 0.29 | −0.0004 (−0.002, 0.001) | 0.538 | 0.773 |
| Resistin | 0.0011 (−0.008, 0.010) | 0.041 | 0.801 | −0.002 (−0.016, 0.012) | 0.538 | 0.777 |
| Serpin E1 | 0.0018 (−0.0019, 0.0054) | 0.154 | 0.338 | 0.001 (−0.006, 0.007) | 0.537 | 0.808 |
| Adiponectin | 0.032 (−0.004, 0.069) | 0.274 | 0.083 | 0.022 (−0.055, 0.098) | 0.545 | 0.560 |
| IL-6 | 0.0013 (−0.00001, 0.00028) | 0.288 | 0.072 | 0.0001 (−0.00008, 0.0003) | 0.569 | 0.223 |
| Leptin | 0.0002 (−0.0024, 0.0029) | 0.028 | 0.868 | 0.0002 (−0.006, 0.006) | 0.560 | 0.950 |
| FABP4 | 0.00004 (−0.00009, 0.00018) | 0.104 | 0.521 | −0.001 (−0.003, 0.001) | 0.552 | 0.348 |
| MIP1β | 0.00044 (−0.0012, 0.0021) | 0.089 | 0.585 | −0.001 (−0.003, 0.001) | 0.566 | 0.239 |
* 95% CI = 95% confidence interval. † Change in EQ5D per unit increase in cytokine. ‡ Change in EQ5D per unit increase in cytokines, including age, body mass index, Kellgren/Lawrence grade, joint space, waist and hip circumference, waist-to-hip ratio and body weight in the regression equation.
Table 6.
Multiple regression models.
| R | p-Value | Unstandardized Coefficients (B) | ||||
|---|---|---|---|---|---|---|
| Cytokine | BW | Constant | Equation | |||
| Amph, BW | 0.434 | 0.023 | −0.108 | −0.0006 | 1.052 | ΔEQ5D = −0.108 [Amph ng/mL] – 0.0006 [BW kg] + 1.053 |
| IL-6, BW | 0.418 | 0.035 | 7.4 × 10−5 | −0.006 | 0.871 | ΔEQ5D = 7.4 × 10−5 [IL-6 ng/mL] – 0.006 [BW kg] + 0.871 |
| Amph, IL-6, BW | 0.470 | 0.035 | −0.142 (Amph) 8.3 × 10−5 (IL-6) | −0.005 | 0.919 | ΔEQ5D = −0.142 [Amph ng/mL] +8.3 × 10−5 [IL-6 ng/mL] −0.005 [BW kg] + 0.919 |
Amph = amphiregulin; BW = body weight.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Nanus, D.E.; Davis, E.T.; Jones, S.W. Pre-Operative Adiposity and Synovial Fluid Inflammatory Biomarkers Provide a Predictive Model for Post-Operative Outcomes Following Total Joint Replacement Surgery in Osteoarthritis Patients. Osteology 2024, 4, 53-63. https://doi.org/10.3390/osteology4020005
AMA Style
Nanus DE, Davis ET, Jones SW. Pre-Operative Adiposity and Synovial Fluid Inflammatory Biomarkers Provide a Predictive Model for Post-Operative Outcomes Following Total Joint Replacement Surgery in Osteoarthritis Patients. Osteology. 2024; 4(2):53-63. https://doi.org/10.3390/osteology4020005
Chicago/Turabian StyleNanus, Dominika E., Edward T. Davis, and Simon W. Jones. 2024. "Pre-Operative Adiposity and Synovial Fluid Inflammatory Biomarkers Provide a Predictive Model for Post-Operative Outcomes Following Total Joint Replacement Surgery in Osteoarthritis Patients" Osteology 4, no. 2: 53-63. https://doi.org/10.3390/osteology4020005
