Relationship Between Activity Level and Knee Function Is Influenced by Negative Affect in Patients Undergoing Cell Therapy for Articular Cartilage Defects in the Knee.

Background: Increased activity level is generally reported to be positively related to improved knee function after knee surgery. However, little research has been conducted into this relationship on an individual patient basis, or the influence of demographic and psychosocial factors such as patient affect-the subjective experience of emotion. Hypothesis: The relationship between postoperative activity level and knee function will vary between patients and will be influenced by the patients' affect and demographic characteristics. Study Design: Cohort study; Level of evidence, 3. Methods: Activity, knee function, demographic, and affect data were collected from patients enrolled in an ongoing trial for the treatment of articular cartilage lesions at preoperative and 2-, 12-, and 15-month postoperative points. Quantile mixed regression modeling was used to determine the patient-to-patient variation in activity level and knee function. Multiple linear regression and partial correlation analyses were performed to determine whether demographic characteristics and patient affect were associated with this variation. Results: A total of 62 patients were included in the study (23 female; 39 male; mean age, 38.3 ± 9.5 years). We found substantial variation between patients in the relationship between activity level and knee function, with most patients (n = 56) demonstrating a positive relation (positive slope), but 6 patients demonstrating a negative relation (negative slope). A negative affect (NA) score was significantly correlated with the slope between activity level and knee function (r S = -0.30; P = .018) and was a significant individual predictor of knee function at 15 months postoperatively (coefficient = -3.5; P = .025). Conclusion: Our results suggest that the relationship between activity level and knee function varies between patients. The patients with a higher NA score were likely to report smaller improvements in knee function with increasing activity levels compared with those with a lower NA score.

Proteomic Analyses of Autologous Chondrocyte Implantation Plasma Highlight Cartilage Acidic Protein 1 as a Candidate for Preclinical Screening.

BACKGROUND: Stratification is required to ensure that only patients likely to benefit receive autologous chondrocyte implantation (ACI). It would be advantageous to identify biomarkers to predict ACI outcome that are measurable in blood, avoiding the need for an invasive synovial fluid harvest. PURPOSE: To assess if proteomic analyses can be used to identify novel candidate blood biomarkers in individuals who respond well or poorly to ACI. STUDY DESIGN: Controlled laboratory study. METHODS: Isobaric tagging for relative and absolute quantitation (iTRAQ) mass spectrometry was used to assess the proteome in plasma pooled from ACI responders (mean Lysholm improvement after ACI, 33; n = 10) or nonresponders (mean, -13; n = 10), collected at the time of surgery for cartilage harvest (stage 1) or implantation of culture-expanded chondrocytes (stage 2). An alternative proteomic method, label-free quantitation liquid chromatography-tandem mass spectrometry, was used to analyze plasma samples (majority matched to iTRAQ) individually. Differentially abundant proteins (±2.0-fold) were analyzed from both proteomic data sets, and markers of interest identified via pooled iTRAQ were validated via immunoassay of individual samples. RESULTS: Protein differences could be detected in the plasma preoperatively between ACI responders and nonresponders (16 proteins; ≥±2.0-fold change; P < .05) using iTRAQ proteomics. The most pronounced plasma proteome shift was evident in response to stage 1 surgery in ACI nonresponders, with 48 proteins being differentially abundant between the procedures. Label-free quantitation liquid chromatography-tandem mass spectrometry analysis of these same plasma samples (nonpooled) resulted in very few proteins being identified that were significantly differentially abundant. However, this work highlighted cartilage acidic protein 1 as being increased preoperatively in nonresponders as compared with responders. CONCLUSIONS: This study is the first to use proteomic techniques to profile the plasma of individuals treated with ACI. Despite iTRAQ analysis of pooled plasmas indicating that there are differences in the plasma proteome between responders and nonresponders to ACI, these findings were not replicated when assessed using an alternative nonpooled technique. This study highlights some of the difficulties in profiling the plasma proteome in an attempt to identify novel biomarkers. Regardless, cartilage acidic protein 1 has been identified as a protein candidate, which is detectable in plasma and can predict outcome to ACI before treatment. CLINICAL RELEVANCE: Candidate plasma protein biomarkers identified in this study have the potential to help determine which patients will be best suited to treatment with ACI.

Identification of Candidate Synovial Fluid Biomarkers for the Prediction of Patient Outcome After Microfracture or Osteotomy.

BACKGROUND: Biomarkers are needed to predict clinical outcomes for microfracture and osteotomy surgeries to ensure patients can be better stratified to receive the most appropriate treatment. PURPOSE: To identify novel biomarker candidates and to investigate the potential of a panel of protein biomarkers for the prediction of clinical outcome after treatment with microfracture or osteotomy. STUDY DESIGN: Descriptive laboratory study. METHODS: To identify novel candidate biomarker proteins, we used label-free quantitation after liquid chromatography-tandem mass spectrometry of dynamic range-compressed synovial fluids (SFs) from individuals who responded excellently or poorly (based on change in Lysholm score) to microfracture (n = 6) or osteotomy (n = 7). Biomarkers that were identified in this proteomic analysis or that relate to osteoarthritis (OA) severity or have predictive value in another early OA therapy (autologous cell implantation) were measured in the SF of 19 and 13 patients before microfracture or osteotomy, respectively, using commercial immunoassays, and were normalized to urea. These were aggrecanase-1 (ADAMTS-4), cartilage oligomeric matrix protein (COMP), hyaluronan (HA), lymphatic vessel endothelial hyaluronan receptor 1 (LYVE-1), matrix metalloproteinase 1 and 3, soluble CD14, S100 calcium binding protein A13, and 14-3-3 protein theta (YWHAQ). Levels of COMP and HA were also measured in the plasma of these patients. To find predictors of postoperative function, multivariable regression analyses were performed. RESULTS: Proteomic analyses highlighted YWHAQ and LYVE-1 as being differentially abundant between the clinical responders/improvers and nonresponders after microfracture. A linear regression model after backward variable selection could relate preoperative concentrations of SF proteins (HA, YWHAQ, LYVE-1), activity of ADAMTS-4, and patient demographic characteristics (smoker status and sex) with Lysholm score 12 months after microfracture. Further, a generalized linear model with elastic net penalization indicated that lower preoperative activity of ADAMTS-4 in SF, being a nonsmoker, and being younger at the time of operation were indicative of a higher postoperative Lysholm score (improved joint function) after osteotomy surgery. CONCLUSION: We have identified biomarkers and generated regression models with the potential to predict clinical outcome in patients treated with microfracture or osteotomy of the knee. CLINICAL RELEVANCE: Candidate protein biomarkers identified in this study have the potential to help determine which patients will be best suited to treatment with microfracture or osteotomy.

Two independent proteomic approaches provide a comprehensive analysis of the synovial fluid proteome response to Autologous Chondrocyte Implantation.

BACKGROUND: Autologous chondrocyte implantation (ACI) has a failure rate of approximately 20%, but it is yet to be fully understood why. Biomarkers are needed that can pre-operatively predict in which patients it is likely to fail, so that alternative or individualised therapies can be offered. We previously used label-free quantitation (LF) with a dynamic range compression proteomic approach to assess the synovial fluid (SF) of ACI responders and non-responders. However, we were able to identify only a few differentially abundant proteins at baseline. In the present study, we built upon these previous findings by assessing higher-abundance proteins within this SF, providing a more global proteomic analysis on the basis of which more of the biology underlying ACI success or failure can be understood. METHODS: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomic analysis was used to assess SF from ACI responders (mean Lysholm improvement of 33; n = 14) and non-responders (mean Lysholm decrease of 14; n = 13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins in iTRAQ and combined iTRAQ and LF datasets were investigated using pathway and network analyses. RESULTS: iTRAQ proteomic analysis confirmed our previous finding that there is a marked proteomic shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥ 2.0-fold change and p < 0.05 between stages I and II in responders and non-responders, respectively). Further, it highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, which were not found in the LF study, 16 of which were altered at baseline. The differential expression of two proteins (complement C1s subcomponent and matrix metalloproteinase 3) was confirmed biochemically. Combination of the iTRAQ and LF proteomic datasets generated in-depth SF proteome information that was used to generate interactome networks representing ACI success or failure. Functional pathways that are dysregulated in ACI non-responders were identified, including acute-phase response signalling. CONCLUSIONS: Several candidate biomarkers for baseline prediction of ACI outcome were identified. A holistic overview of the SF proteome in responders and non-responders to ACI  has been profiled, providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.

The Absence of Detectable ADAMTS-4 (Aggrecanase-1) Activity in Synovial Fluid Is a Predictive Indicator of Autologous Chondrocyte Implantation Success.

BACKGROUND: Autologous chondrocyte implantation (ACI) is used worldwide in the treatment of cartilage defects in the knee. Several demographic and injury-specific risk factors have been identified that can affect the success of ACI treatment. However, the discovery of predictive biomarkers in this field has thus far been overlooked. PURPOSE: To identify potential biomarkers in synovial fluid and plasma that can be used in the preoperative setting to help optimize patient selection for cell-based cartilage repair strategies. STUDY DESIGN: Controlled laboratory study. METHODS: Fifty-four ACI-treated patients were included. Cartilage oligomeric matrix protein (COMP), hyaluronan, soluble CD14 levels, and aggrecanase-1 (ADAMTS-4) activity in synovial fluid and COMP and hyaluronan in plasma were measured. Baseline and postoperative functional outcomes were determined using the patient-reported Lysholm score. To find predictors of postoperative function, linear and logistic regression analyses were performed. The dependent variables were the baseline and postoperative Lysholm score; the independent variables were patient age and body mass index, defect location, defect area, having a bone-on-bone defect, type of defect patch (periosteum or collagen), requirement of an extra procedure, and baseline biomarker levels. RESULTS: The mean baseline Lysholm score was 47.4 ± 17.0, which improved to 64.6 ± 21.7 postoperatively. The activity of ADAMTS-4 in synovial fluid was identified as an independent predictor of the postoperative Lysholm score. Indeed, simply the presence or absence of ADAMTS-4 activity in synovial fluid appeared to be the most important predictive factor. As determined by contingency analysis, when ADAMTS-4 activity was detectable, the odds of being a responder were 3 times smaller than when ADAMTS-4 activity was not detectable. Other predictive factors were the baseline Lysholm score, age at ACI, and defect patch type used. CONCLUSION: The absence of ADAMTS-4 activity in the synovial fluid of joints with cartilage defects may be used in conjunction with known demographic risk factors in the development of an ACI treatment algorithm to help inform the preclinical decision.

Autologous chondrocyte implantation-derived synovial fluids display distinct responder and non-responder proteomic profiles.

BACKGROUND: Autologous chondrocyte implantation (ACI) can be used in the treatment of focal cartilage injuries to prevent the onset of osteoarthritis (OA). However, we are yet to understand fully why some individuals do not respond well to this intervention. Identification of a reliable and accurate biomarker panel that can predict which patients are likely to respond well to ACI is needed in order to assign the patient to the most appropriate therapy. This study aimed to compare the baseline and mid-treatment proteomic profiles of synovial fluids (SFs) obtained from responders and non-responders to ACI. METHODS: SFs were derived from 14 ACI responders (mean Lysholm improvement of 33 (17-54)) and 13 non-responders (mean Lysholm decrease of 14 (4-46)) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Label-free proteome profiling of dynamically compressed SFs was used to identify predictive markers of ACI success or failure and to investigate the biological pathways involved in the clinical response to ACI. RESULTS: Only 1 protein displayed a ≥2.0-fold differential abundance in the preclinical SF of ACI responders versus non-responders. However, there is a marked difference between these two groups with regard to their proteome shift in response to cartilage harvest, with 24 and 92 proteins showing ≥2.0-fold differential abundance between Stages I and II in responders and non-responders, respectively. Proteomic data has been uploaded to ProteomeXchange (identifier: PXD005220). We have validated two biologically relevant protein changes associated with this response, demonstrating that matrix metalloproteinase 1 was prominently elevated and S100 calcium binding protein A13 was reduced in response to cartilage harvest in non-responders. CONCLUSIONS: The differential proteomic response to cartilage harvest noted in responders versus non-responders is completely novel. Our analyses suggest several pathways which appear to be altered in non-responders that are worthy of further investigation to elucidate the mechanisms of ACI failure. These protein changes highlight many putative biomarkers that may have potential for prediction of ACI treatment success.