{"paper_id":"ced985df-2cff-4572-8781-f26f0d37e224","body_text":"Insights into knee post-traumatic osteoarthritis pathophysiology from the relationship of serum biomarkers to radiographic features in the ADVANCE cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Insights into knee post-traumatic osteoarthritis pathophysiology from the relationship of serum biomarkers to radiographic features in the ADVANCE cohort Oliver O'Sullivan, Ana M Valdes, Fraje Watson, Stefan Kluzek, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6120483/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Arthritis Research & Therapy → Version 1 posted 9 You are reading this latest preprint version Abstract Introduction Post-traumatic osteoarthritis (PTOA) is a complex condition with multiple pathological processes at play. Molecular biomarkers can enable a better understanding of these processes, thus enhancing case endotyping, phenotyping, personalised care and drug discovery. The longitudinal ArmeD SerVices TrAuma and RehabilitatioN OutComE (ADVANCE) study offers the opportunity to develop insights into PTOA pathophysiology using a panel of extra-cellular matrix turnover, inflammatory and metabolic biomarkers and their cross-sectional (associative) and longitudinal (predictive) relationship to features of radiographic PTOA in a cohort of young, male, severely injured servicepersonnel. Methods Using serum and radiographic data gathered in the baseline (8-years) and first follow-up visit (11-years) post-injury of ADVANCE (n = 1145), two analyses were undertaken. Firstly, cross-sectional univariate analysis between serum COMP, CTX-II, PIIANP, IL-1b, IL-17a, TNF-a, leptin and adiponectin and radiographic features (joint space narrowing (JSN), osteophytes and sclerosis), followed by the longitudinal prediction of new or progression of these three radiographic features using LASSO to select predictors. The area under a ROC curve (AUROC) was computed. Results Complete radiographic and serum case data in n = 872 male British servicemen, aged 34.5 (5.5) at baseline and 38.3 (5.4) at follow-up were analysed. Those with JSN had significantly higher concentrations of leptin (FDR-corrected q-value, q = 0.04). COMP had an AUROC of 0.604 (0.543,0.664) for new cases of JSN, COMP, IL-1β and leptin had an AUROC 0.586 (0.524,0.646) for new osteophytes, and TNF-α, IL-1β and adiponectin had an AUROC 0.590 (0.520,0.659) for new sclerosis. Conclusion This large, unique study suggests different pathological processes underpinning each radiographic feature of PTOA, including predominant unbalanced ECM-catabolism and inflammation contributing to JSN, ECM-catabolism and increased inflammation contributing to osteophyte development and an inflammation-predominant process contributing to subchondral sclerosis. pathophysiology serum biomarkers anabolism catabolism inflammation post-traumatic osteoarthritis Figures Figure 1 Figure 2 Introduction Osteoarthritis is a complex, multifaceted disease influenced by modifiable and non-modifiable risk factors, resulting in a clinical continuum from a prodromal asymptomatic condition to a highly disabling painful clinical syndrome( 1 ). It has been estimated that 13% of knee OA can be attributed to previous trauma ( 2 ), with certain injuries, such as anterior cruciate ligament (ACL) rupture, meniscal injury, or intra-articular fracture, carrying a significant risk of subsequent post-traumatic OA (PTOA), presenting within a few years after injury, with a period of intermittent symptomatic recovery prior to symptomatic and functional decline ( 3 – 5 ). The initial traumatic episode can result in localised disruption of articular cartilage, cartilage fissures, and chondrocyte death, accompanied by a post-traumatic inflammatory response and synovitis, as well as concurrent damage affecting the biomechanical function of the joint, such as fractures ( 6 ). Immediately after this, multiple signalling pathways activate, resulting in cartilage matrix degradation and synovial inflammation, leading to repair, remodelling, and adaptation. However, aberrant mechanistic pathways can contribute to a lack of repair and remodelling, inadequate adaptation, and the subsequent development of PTOA, with altered biomechanics, metabolism, and low-grade inflammation resulting in disturbed joint homeostasis. Unlike idiopathic OA, where pathological articular cartilage changes trigger negative surrounding structural changes, it is possible that in early PTOA, structural changes influence local bone and cartilage compositional changes ( 3 , 6 – 8 ). These changes involve cartilage matrix macromolecule synthesis or subchondral bone resorption, mediated by cytokines, and leave signals which can be detected at a molecular level ( 9 ). In recent decades, there has been a research focus on early identification and preventative strategies alongside a renewed focus on drug discovery to slow the impact of this common disabling condition, especially through the use of biological markers (biomarkers), defined by the National Institutes for Health (NIH) as ‘ a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention’ ( 10 ). Biomarkers have been categorised by type (type 0: elucidate disease natural history, type 1: measuring intervention effect, type 2: a surrogate endpoint)( 10 ), future role (burden of disease, investigative, prognostic, efficacy of intervention, diagnosis or safety, BIPEDS)( 11 ) and level of quantification (exploration, demonstration, characterisation, surrogacy)( 12 ). Multiple biomarker types are under investigation, including molecular ( 13 ), imaging ( 14 ) and biomechanical ( 15 ), which are compared to either structural change, through the use of x-ray, including Kellgren-Lawrence (KL) grades ( 16 ), or pain and symptoms, through the use of validated patient-reported outcome measures (PROMs), such as the Knee injury and OA Outcome Score (KOOS)( 17 ). Each of these modalities offers a different insight into PTOA pathophysiology and clinical picture; however, challenges remain within each modality, including a clear understanding of how the observed metric relates to the underlying disease process. Certain molecular biomarkers relate to future pain or disease progression, with academic-regulatory-pharmacological cross-sector coalescence around the most promising biomarkers to improve their validation and qualification spearheaded by the NIH ( 18 , 19 ). Eight molecular biomarkers significantly predicted case status, with three (urinary C-terminal cross-linked telopeptide of type II collagen, CTX-II, serum hyaluronan (HA), serum crosslinked N-telopeptide of type I collagen, NTX-1) offering the best prediction of future OA ( 18 ). Phase 2 of this work, the OA PROGRESS study, is nearing completion, with the ambition to enable the validation of biochemical (as well as imaging) measures for prognosis and prediction of disease progression over 48 months using thousands of pooled participant data from existing trial data( 20 ). However, the vast majority of those included in these pooled data are aged ≥ 60 years old ( 20 ), and whilst OA does become more common with age, it can commence and present during any stage of adult life, especially in those with a traumatic cause. In nearly 40% of those with OA, symptoms commenced before 45-years-old, with those experiencing symptoms prior to 35 years old potentially waiting a decade before receiving a diagnosis( 21 ). Furthermore, certain occupational groups, such as physical jobs, sportspeople and emergency services, are associated with an increased risk of PTOA with increased surveillance and intervention required to mitigate this( 22 – 24 ). Additionally, after exposure to major trauma, PTOA can present far sooner, in less than two years( 3 ). The Armed Services Trauma Rehabilitation Outcome (ADVANCE) study is a prospective, longitudinal cohort study which aims to understand the long-term physical and psycho-social effects of combat injury in 1145 British servicemen, half of whom were seriously injured during the Afghanistan war. The ADVANCE study offers, a young, heterogenous and high-risk cohort in which to investigate PTOA further to understand the relationship between molecular biomarkers and pathophysiology( 25 ). Earlier work demonstrated increased rates of knee OA across the cohort, up to 4x in those with combat injury or lower-limb loss( 26 ), but also nearly 20% of the non-injured comparison British servicemen( 27 ). Further serum biomarker analysis revealed adipokines were able to classify those experiencing pain. Although serum biomarker analysis were not able to differentiate those with and without OA, it did identified that there was no molecular difference between those with OA in the exposed group (PTOA) and in the unexposed group (early-onset idiopathic OA, iOA)( 27 ). We speculate that the inability of the serum biomarkers to differentiate OA cases was, in part, due to the predominance of early changes (70% of all cases were KL grade 1), and that the molecular similarities between post-traumatic and idiopathic aetiologies were due to the length of time since injury. Other cohorts have demonstrated an increased post-injury risk, plateauing as time from injury increases ( 24 , 28 ), a hypothesis supported in the ADVANCE cohort by no increased rates of radiographic OA between exposed and unexposed groups between the baseline visit (8 years from injury) and the first follow-up visit three years later (11 years from injury) ( 29 ). Therefore, the ADVANCE study offers an exciting opportunity to further explore the pathophysiological processes of OA present in a younger cohort, using the individual components of the OARSI radiological atlas (joint space narrowing (JSN), osteophytes, sclerosis)( 30 ) and panel of extracellular matrix turnover, inflammatory and metabolic serum biomarkers, using data from the 8-year and recently completed 11-year from major trauma or matched deployment visits. This unique cohort will enable further analysis to elucidate the natural course of the disease, investigate and offer prognostic ability, and add to the evidence base for the further quantification of established serum biomarkers. The hypothesis of this study is that serum biomarkers measuring different pathophysiological processes will be associated and predictive of different radiological features of PTOA. The aim is two-fold; firstly, to undertake a cross-sectional analysis of the panel of candidate serum biomarkers to OARSI radiographic atlas features (JSN, osteophytes, sclerosis); and secondly, to undertake a longitudinal analysis to ascertain the ability of these biomarkers to predict individual radiological feature development or progression over three years. Methods Ethics Favourable opinion for ADVANCE was granted by MoD Research Ethics Committee (MODREC:357PPE12) with subsequent approval from the University of Nottingham Faculty of Medicine and Health Sciences REC (UoN FMHS 170–1122). Study participation is voluntary, with written informed consent from each participant at each study visit, with the study performed in accordance with the Declaration of Helsinki. PPI Public and Patient Involvement is undertaken using a variety of methods, including face-to-face and remote participant panels, feedback questionnaires for each study visit, newsletters, participant-focussed impact reports, and the study website ( www.advancestudydmrc.org.uk ). These have helped refine study design, logistics and tailor further research questions relevant to the interests of the participants. Participants ADVANCE is a longitudinal cohort study monitoring the long-term physical and psychosocial outcomes following combat injury in UK military personnel who served in Afghanistan, involving 579 participants who sustained combat injuries requiring aeromedical evacuation to the UK (Exposed), and 566 participants not exposed to combat injury who were frequency-matched for age, rank, role, service, and deployment (Unexposed). Female military personnel are excluded due to the number sustaining combat injuries being too low to generate sufficient statistical power. Additional information on recruitment and inclusion/exclusion criteria can be found in the protocol paper and previously published papers( 25 – 27 ). Potential exposed participants were identified using military health records, with frequency matched unexposed comparison participants identified by the Defence Statistics (Health) team. Data collection Study visits occurred at the Defence Medical Rehabilitation Centre Headley Court (2015–2018) or Stanford Hall (2018–2024) over the period of one day, with participants fasted and absent from caffeine and alcohol for at least 8 hours before the visit. Trained research nurses collect a range of assessments, including sociodemographic, anthropometric, medical history (including existing medical problems, combat trauma, and significant family history), serum sampling, and radiographs of their knees. Study data were collected and managed using Research Electronic Data Capture (REDCap), a secure, web-based software platform( 31 ). This study reports data collected at the baseline (8-years post-injury/deployment) and first follow-up (11-years post-injury/deployment visit of the ADVANCE study. Radiography Semi-flexed (7–10°) posterior-anterior views of all possible participant knees were taken using a Synaflexer X-ray positioning frame (Synarc Inc, San Francisco, California). The tibiofemoral joint was scored using the Osteoarthritis Research Symposium International (OARSI) atlas( 30 ), using KOALA with manual checking, which grades JSN, sclerosis and osteophytes using a 4-point scale (0 – none, 1 – mild, 2 – moderate, 3 – severe). No member of the radiographic reporting team was aware of the participant’s clinical status. The FDA-approved Knee OA Labelling Assistant (KOALA, Image Biopsy Lab, Vienna, Austria) with manual checking scored the x-ray, offering an accuracy of 76%, 84%, 60%, sensitivity of 97%, 88%, 97% and specificity of 41%, 73% and 24% for all grades JSN, osteophyte and sclerosis, rising to 84% and 88%, 71% and 77%, 94% and 92%, respectively, in the presence of osteophytes and sclerosis (≥ 1)( 32 ). The aided AI KOALA tool improves reader agreement rates by 1.37, 1.59, and 1.42-fold when assessing JSN, osteophyte or sclerosis grade ( 33 ). Analyses were performed using the presence of any individual measures of the OARSI atlas (JSN, osteophytes, sclerosis) to ascertain if any specific biomarkers offered improved identification of different pathological mechanisms in the cartilage, surface or subchondral bone matrix. When participants had two variables (from both knees), the index knee score signifying more advanced rOA was selected (higher JSN/osteophyte/sclerosis grade). Progression was defined as an increase in individual radiographic feature (JSN/osteophyte/sclerosis) by ≥ 1 at Follow-up in a knee with at least JSN/osteophyte/sclerosis ≥ 1 at Baseline. Incidence was defined as the presence of a radiographic feature of knee OA (JSN/osteophyte/sclerosis ≥ 1) at Follow-up in a knee that was JSN/osteophyte/sclerosis 0 at Baseline. Each feature was taken in turn and analysed separately to understand the relationship between the serum biomarker and the possible pathophysiological change reflected by the radiographic feature. Biomarker analysis Participants underwent serum sampling with fasted blood taken from the antecubital fossa using the Vacutainer system. It was centrifuged at 3500rpm for 10 minutes, and then aliquoted to be stored into cryovials and stored in monitored freezers at -80°. Frozen baseline samples were analysed by Affinity Biomarker Laboratory (ABL), London, for cartilage oligomeric protein (COMP), CTX-II, N-propeptide of collagen IIA (PIIANP), interleukin (IL)-1b, IL-17a, tumour necrosis factor (TNF)-a, leptin and adiponectin via Meso Scale Discovery (MSD) or enzyme-linked immunosorbent assay (ELISA). This panel of serum candidate biomarkers, only sampled at baseline, were chosen to examine different pathological mechanisms, including aberrant tissue turnover, inflammatory dysfunction and metabolic dysregulation. Results underwent quality assurance and control by ABL, who were also not aware of the participants clinical status, using three internally identified quality control samples and two kit controls. The worst reported intra- or inter-variability coefficient of variation for each biomarker were; MSD: IL-17a < 9.5%, IL-1β < 7%, TNF-a < 15%; ELISA: COMP < 12%, leptin < 7%, adiponectin < 8%, CTX-II < 11%, PIIANP < 6%. Any biomarker concentration level below the lower limit of quantification (LLOQ) was given a value halfway between zero and the LLOQ threshold, with those above the upper LOQ (ULOQ) given ULOQ threshold + 1, required for IL-17a (< 0.54 = 0.27, n = 24), PIIANP (< 5.9 = 2.95 n = 23, > 1000 = 1001, n = 9), CTX-II (< 0.1 = 0.05, n = 421), and IL-1β (< 0.043 = 0.0215, n = 713). Statistical analysis All data were screened for normality visually using histograms, with parametric and non-parametric testing used accordingly and presented as mean (standard deviation, SD) and median (interquartile range, IQR), respectively. As a result of earlier work, demonstrating no molecular differences between those with OA in the exposed and non-exposed groups, participants are dichotomised due to presence of individual radiographic OA feature, not exposure status. The analysis was performed in two parts, to address each aim in turns. First, an initial descriptive analysis was performed to identify presence and amount of individual OA features, with correlation analysis performed with biomarkers standardised to a mean = 0 and SD = 1 to visualise patterns in the data (Spearman’s or Pearson’s, full results in Supplementary File 1). Univariate analysis was subsequently performed, depending on normality and the presence or absence of each individual radiographic feature (Wilcoxon rank sum and Student’s t test). Unadjusted analyses were initially performed using the baseline data, followed by adjusted, with the confounders age, body mass, time from injury/deployment, exposure to trauma, military rank (as a proxy for socio-economic status, SES( 34 , 35 )) and ethnicity adjusted for. This was performed by transforming the biomarkers using their natural logarithm and adjusted for the confounders using a regression model, with studentised residuals created and taken forward for analysis. Within an athletic population, the body mass index (BMI) can ‘overscore’ individuals with a high muscle mass; therefore, a body shape index (ABSI)( 36 ), calculated with BMI and waist circumference, was utilised, which gives a balanced reflection of body weight and central adiposity. Time from injury/deployment was measured from the participant’s index deployment. Next, using longitudinal data from the ADVANCE 11-year follow up visit, an analysis of the predictive value of the serum biomarkers was performed. Participant x-rays were assessed and dichotomised for either the incidence or progression of each individual radiographic feature as described above, with correlation analysis again used with standardised biomarkers to visualise patterns (full results, Supplementary File 1). Due to the numbers of participants within each of these groups, a least absolute shrinkage and selection operator (LASSO) variable selection model was performed to identify significant relationships between serum biomarkers and radiographic features. When significant predictors were identified, multivariate logistic regression was performed, with Nagelkerke’s R2 and area under the receiver operator curve (AUROC) reported (further regression results in Supplementary File 2). Due to the small numbers in groups, full adjustment was not possible, with only unadjusted results reported. The ADVANCE study recruited n = 1145 male British servicemen (exposed n = 579, unexposed n = 566) at baseline, with n = 1052 attending the first follow-up visit (92%). A whole case approach was adopted for this analysis, therefore, the n = 190 participants who did not have x-ray at both time points or biomarker data available were excluded, inclusive of those who did not attend follow-up (n = 93). In addition, to prevent confounding due to the likely different pathological processes at play for those sustaining lower-limb loss and subsequent OA( 37 ), the n = 161 individuals sustaining amputations were also excluded, leaving 872 participants. Given the large number of comparisons performed in this exploratory study, results have been additionally corrected using the Benjamini and Hockberg method, with results corrected using the false discovery rate (q-value)( 38 ), with the significance rate set at 0.1. Data is presented with the unadjusted, uncorrected p-value and the fully adjusted, corrected q-value. Presenting data both in its unadjusted and adjusted forms allows other studies to compare results and enables the effect of OA to be more accurately partitioned. Analyses were performed in Stata 18.5 (StataCorp LLC, Texas) and GraphPad Prism 10 (Dotmatics, Boston). Results Eight-hundred and seventy-two male participants, aged 34.5 (5.5) at the 8-year and 38.3 (5.4) at the 11-year visit are included, at a mean 9.0 (2.2) years from injury (for the 42% of injured participants) or index deployment at baseline and 3.3 years (0.6) between baseline and follow-up (Table 1 ). Table 1 Participant demographics Total (n = 872) Age, yrs (BL) 34.5 (5.5) Age, yrs (FU) 38.3 (5.4) BMI, kg/m 2 (BL) 27.6 (3.5) BMI, kg/m 2 (FU) 28.2 (3.7) Abdo. circum., cm (BL) 93.0 (87.5-100.5) Abdo. circum., cm (FU) 96.0 (90.0-103.0) Exposed, n= (%) 364 (41.7%) Military Rank, n= (%) Junior NCO 542 (62%) Senior NCO 211 (24%) Officer 119 (14%) Time from injury, yrs 9.0 (2.2) Time to follow-up, yrs 3.3 (0.6) Caucasian, n= (%) 771 (88.5%) BL: Baseline, FU: Follow up, yrs: years, BMI: body mass index, kg: kilograms, M: metres, abdo circum: abdominal circumference, cm – centimetres, NCO – non-commissioned officer. Data presented as mean ± standard deviation, median (interquartile range) or n= (%) At baseline, n = 331 (38%) participants had JSN, n = 165 (19%) had osteophytes and n = 74 (9%) sclerosis evident on their knee radiographs, rising to n = 370 (42%), n = 233 (27%) and n = 117 (13%) by follow-up, respectively (Table 2 ). Across all categories, the vast majority were Grade 1 (Table 2 ), with a mild increase in severity between study visits, with 3% of JSN, 10% of osteophyte and 16% of sclerosis cases increasing by ≥ 1 grade (Table 3 ). In individuals without specific radiological change at the 8-year visit, 19% developed new JSN, 15% developed new osteophytes and 9% developed new sclerosis by the 11-year visit (Table 3 ). Table 2 Rates of joint space narrowing (JSN), osteophytes and sclerosis in the ADVANCE cohort at baseline and follow-up Baseline Follow-up JSN, n= (%) 331 (38.0%) JSN, n= (%) 370 (42.4%) JSN grade, n= (%) JSN grade, n= (%) 0 541 (62%) 0 502 (58%) 1 307 (35%) 1 343 (39%) 2 21 (2%) 2 20 (2%) 3 3 (0%) 3 7 (1%) Osteophytes, n= (%) 165 (18.9%) Osteophytes, n= (%) 233 (26.7%) OP grade, n= (%) OP grade, n= (%) 0 707 (81%) 0 639 (73%) 1 132 (15%) 1 191 (22%) 2 16 (2%) 2 19 (2%) 3 17 (2%) 3 23 (3%) Sclerosis, n= (%) 74 (8.5%) Sclerosis, n= (%) 117 (13.4%) Scl grade, n= (%) Scl grade, n= (%) 0 798 (92%) 0 755 (87%) 1 64 (7%) 1 98 (11%) 2 10 (1%) 2 18 (2%) 3 - 3 1 (0%) JSN: Joint space narrowing, Scl: Sclerosis, OP: Osteophytes. Data presented as n= (%) Table 3 Rates of new or progressive joint space narrowing (JSN), osteophytes and sclerosis between baseline and follow-up JSN Osteophytes Sclerosis New Progress New Progress New Progress 105/541 (19%) 10/331 (3%) 104/707 (15%) 17/165 (10%) 69/798 (9%) 12/74 (16%) Figure 1 shows the cross-sectional correlation between the panel of serum biomarkers and individual radiographic features performed at the 8-year visit (full results, Supplementary File 1). Those with JSN changes at baseline had significantly higher levels of COMP (p = 0.002) and leptin (p < 0.001) (Table 4 ). After adjustment and correction, the difference in leptin remained significant (q = 0.04). COMP (p < 0.001) and leptin (p = 0.006) were significantly higher, and PIIANP significantly lower (p = 0.009) between those with and without osteophytes at baseline; however, after adjustment and correction, no differences remained significant (Table 4 ). In those with sclerosis at baseline, leptin was higher (p < 0.001), with PIIANP (p = 0.036) and adiponectin (p = 0.015) both significantly lower than those without (Table 4 ). After adjustment and correction, neither remained significant. Figure 2 shows the longitudinal correlation between the panel of serum biomarkers and the new or progressive cases of the individual radiographic features (full results, SF1). LASSO selected COMP as a predictor for new cases of JSN, with an area under the receiver curve (AUROC) of 0.604 (95% confidence interval, CI, 0.543,0.664) and R2 0.018 (Supplementary File 2). COMP, IL-1β and leptin were selected as predictors of new osteophyte cases, with an AUROC = 0.586 (95% CI 0.525,0.646), R2 0.018 (Supplementary File 2). TNF-α, IL-1β and adiponectin were selected as predictors for new cases of sclerosis, AUROC = 0.590 (95% CI 0.520,0.659), R2 0.022 (Supplementary File 2). No biomarkers were selected by LASSO for the prediction of JSN, osteophyte or sclerosis progression. Table 4 Differences in serum biomarker concentration between those with and without joint space narrowing, osteophytes and sclerosis within the ADVANCE cohort Total No JSN JSN p-value q-value No OP OP p-value q-value No Scl Scl p-value q-value N = 872 N = 541 N = 331 N = 707 N = 165 N = 798 N = 74 IL-1β 0.02 (0.02–0.06) 0.02 (0.02–0.06) 0.02 (0.02–0.06) 0.713~ 0.811~ 0.02 (0.02–0.06) 0.02 (0.02–0.05) 0.340~ 0.402~ 0.02 (0.02–0.06) 0.02 (0.02–0.05) 0.490~ 0.678~ TNF-α 1.94 (0.61) 1.92 (0.60) 1.97 (0.62) 0.280# 0.527# 1.95 (0.64) 1.92 (0.46) 0.640# 0.959# 1.94 (0.62) 1.94 (0.46) 0.980# 0.825# IL17-α 1.27 (0.97–1.79) 1.26 (0.96–1.71) 1.31 (0.97–1.87) 0.210~ 0.512# 1.27 (0.96–1.78) 1.30 (1.00-1.86) 0.400~ 0.690# 1.27 (0.96–1.78) 1.38 (1.09–1.86) 0.106~ 0.794# CTX-II 0.21 (0.05–0.64) 0.20 (0.05–0.65) 0.21 (0.05–0.64) 0.700~ 0.512~ 0.21 (0.05–0.64) 0.20 (0.05–0.63) 0.450~ 0.690~ 0.20 (0.05–0.63) 0.32 (0.05–0.72) 0.390~ 0.828~ Leptin 5.59 (2.99–9.04) 5.08 (2.52–8.44) 6.40 (3.59–9.67) < 0.001~ 0.04~ 5.36 (2.88–8.85) 6.26 (4.00-10.12) 0.006~ 0.402~ 5.44 (2.88–8.86) 7.02 (4.31–11.52) < 0.001~ 0.108~ COMP 273.90 (87.73) 266.77 (85.28) 285.55 (90.51) 0.002# 0.512# 268.77 (85.66) 295.87 (93.19) < 0.001# 0.402# 273.02 (88.00) 283.40 (84.66) 0.330# 0.825# Adipo 6.43 (4.60) 6.54 (4.44) 6.27 (4.84) 0.400# 0.512# 6.56 (4.54) 5.90 (4.79) 0.098# 0.402# 6.55 (4.73) 5.19 (2.39) 0.015# 0.108# PIIANP 110.50 (74.30-163.95) 114.20 (74.90-167.10) 103.90 (73.70-159.40) 0.190~ 0.977# 113.30 (78.40-167.30) 97.20 (65.20-151.90) 0.009~ 0.402# 111.75 (75.30-164.90) 91.60 (64.80-153.70) 0.036~ 0.678# IL: Interleukin, TNF: Tumour necrosis factor, CTX-II: C-terminal cross-linked telopeptide of type II collagen, COMP: cartilage oligomeric protein, PIIANP: N-propeptide of collagen IIA, Adipo: Adiponectin. JSN: Joint space narrowing, OP: osteophytes, Scl: Sclerosis. ~ Wilcoxon rank sum test, # Student’s t-test. Unadjusted p-value and adjusted, corrected q-value (for age, body mass, time from injury/deployment, exposure to trauma exposure, socio-economic status and ethnicity, and multiple testing using false discovery rate) Significant results highlighted in bold. Discussion This large unique cohort offers insights into the pathophysiological processes leading to the radiographic presentation of PTOA after severe trauma. The study shows that different markers, reflective of different molecular processes, influence the post-traumatic onset of the three key radiographic features of OA, namely JSN, osteophytes and sclerosis. Cross-sectionally, leptin was significantly higher in those with JSN. Longitudinally, COMP offered some predictive value for the new development of JSN, with panels of COMP, IL-1β and leptin, and TNF-α, IL-1β and adiponectin doing the same for new osteophyte and sclerosis, respectively. These differing biomarkers open a window into the underlying pathomechanisms contributing to the early PTOA structural changes that occur after trauma. PTOA is of particular interest to researchers, due to presentation in a younger population, with fewer co-morbidities, and a clear initiating event. PTOA pathological processes likely represent a failure of initial injury repair and/or remodelling, and altered joint homeostasis with a resultant imbalance between anabolism and catabolism. It has an accelerated pathophysiological process, present within a few years( 4 ), potentially accelerated further following major trauma (such as in military personnel)( 3 ). As a result, military individuals who have sustained significant injuries during combat are extremely high-risk, and are useful to study from a PTOA mechanistic point of view. Investigating individual radiological features can provide possible explanations of each pathway and therefore, an individual’s underlying endotype. A better understanding of endotypes, just like phenotypes, allows a personalised approach for interventions or improvements in drug discovery trial recruitment( 22 ). Recommended as a primary endpoint for structural change by the European Medicines Agency and the Federal Drugs Agency ( 39 , 40 ), JSN is considered a proxy measure for depth, health, and integrity of hyaline articular cartilage. During the PTOA development, articular cartilage undergoes loss in tensile strength and pressure absorption, likely as a result of a mismatch between ECM catabolism and anabolism, and subsequent disruption of homeostasis, in the context of chronic inflammation( 1 , 9 ). This study supports those findings with COMP seen to have an AUROC of 0.604 for new cases of JSN, and whilst the R2 was very low, as were the correlations seen in Figs. 1 and 2 , the increase in COMP suggests increased, and unbalanced, catabolic action( 41 ). In addition, leptin is higher in those with JSN, an adipokine known to influence OA via an inflammatory mechanism influencing cartilage catabolism further( 42 ). Osteophytes, initially cartilaginous outgrowths which subsequently undergo bony ossification, can be an early sign of OA development, and are created in response to load to increase joint stability and compensate for physiological demand ( 43 ). Joint malalignment, either due to biomechanical influence, co-existing disease, or joint injury, can be initially mitigated by osteophytes, commonly at joint margins, before pathological adaption occurs often due to increased instability or increased load (including body mass)( 44 ). The increased COMP and PIIANP levels for those with osteophytes at baseline support the role of ECM turnover in this process, though subsequent adjustment and correction made these differences non-significant. Three biomarkers, COMP, IL-1β and leptin, offered an AUROC of 0.586, with this finding, and the correlations in Figs. 1 and 2 , further demonstrating the imbalance of cartilage and collagen catabolism and anabolism in the presence of inflammation. Subchondral bone sclerosis can be overlooked in the PTOA process, especially given the focus on understanding the processes underpinning and driving articular cartilage degradation( 45 , 46 ). The proinflammatory cascade, triggered by chondrocytes in response to cartilage damage, can lead to subchondral bone turnover and increased vascularisation, ending with bone marrow lesions and sclerosis( 46 ). However, subchondral bone itself can drive cartilage degeneration, with bone-generated cytokines (including IL-1β and-6) and growth factors (such as insulin-like growth factor-1), passing through the tidemark and driving cartilage metabolism( 46 ), hence trabecular bone modelling is a validated marker of PTOA progression( 47 ). In addition, adipokines have been seen to be associated with remodelling in OA, and the formation of fibrosis in other conditions( 48 , 49 ), with post-traumatic fibrosis likely to explain this finding. This study found that the unadjusted adipokines, leptin and adiponectin were significantly higher and lower, respectively in those with sclerosis (non-significant post-adjustment/correction), with TNF-α, IL-1β and adiponectin offering an AUROC of 0.59 for new cases, providing an molecular insight into trabecular and subchondral bone remodelling It is important to contextualise these results. Given the R2 and correlation values, there is likely limited clinical applicability. However, what is notable is that these findings are all in a cohort in their 30’s, male, and with a predominance for early radiographic change, thus, these data offer novel insights into the complex PTOA pathophysiology. It is expected that, as the cohort progresses, the rates and severity will increase further (as has begun between baseline and follow-up, Table 3 ), offering further insight into PTOA processes. These initial results demonstrate that there are distinct pathophysiological processes at play for each component of PTOA structural change, hence different interventions, especially pharmacological ones, might be required for distinct structural endotypes, and subsequent phenotypes( 13 ). Given the significant risk of PTOA, especially in those exposed to major trauma, then proactive interventions should be considered, and now different pathological processes have been identified using biomarker clusters, then these pathways should be considered for targeted multi-modal intervention. These results offer a foundation of understanding, which need to be better demarcated. This will be possible as more follow-up visits are completed in this large, unique cohort, with advanced analysis techniques, such as proteomic analysis to better understand underlying pathological processes( 50 ). There are strengths and limitations to this study. A key strength is the size of population (n = 872), and whilst the all-male study aged in their 30’s is a limitation, this is an under-represented population in OA research. Another strength of this work is the longitudinal nature of the data. Weaknesses include a lack of a replication cohort, the single radiographic view of the knee, which might contribute to underscoring of OA features, and the floor and ceiling effect of the serum biomarkers, which is likely to reduce the statistical power to detect stronger correlations with radiographic features. Conclusion This study reports the relationship between a panel of candidate ECM turnover, inflammatory and metabolic serum biomarkers and the individual radiographic features of knee OA in a large, young, male cohort over multiple timepoints. In line with the prespecified hypothesis, different clusters of biomarkers had relationships with each feature, suggesting different pathological processes at play, including predominant unbalanced ECM-catabolism in the presence of inflammation contributing to JSN, ECM-catabolism and increased inflammation contributing to osteophyte development and an inflammation-predominant process contributing to subchondral sclerosis. Declarations Ethics approval and consent to participate Favourable opinion for ADVANCE was granted by MoD Research Ethics Committee (MODREC:357PPE12) with subsequent approval from the University of Nottingham Faculty of Medicine and Health Sciences REC (UoN FMHS 170–1122). Study participation is voluntary, with written informed consent from each participant at each study visit, with the study performed in accordance with the Declaration of Helsinki. Consent for publication All study participants consented to publication of their anonymised data. Funding The ADVANCE Study is funded through the ADVANCE Charity. Key contributors to the charity are the Headley Court Charity (principal funder), HM Treasury (LIBOR Grant), Help for Heroes, Nuffield Trust for the Forces of the Crown, Forces in Mind Trust, National Lottery Community Fund, Blesma – The Limbless Veterans, and the UK Ministry of Defence. Additional funding for this work was provided by Versus Arthritis (21076), and the UK Ministry of Defence (2122.030). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. Author Contribution AMJB and ANB conceived the study. OOS and ANB gathered data. OOS analysed the data with support from AMV and FW. OOS drafted the manuscript, with support from FW and critical input from all authors. SK, AMV, ANB and AMJB offered expert insight and senior guidance, with OOS, SK, ANB, AMJB involved in acquisition of funding. All authors agreed the final version and are accountable for accuracy and integrity. OOS acts as the guarantor and corresponding author. Acknowledgement We wish to thank all of the research staff at both Headley Court and Stanford Hall who helped with the ADVANCE study, including Emma Coady, Susie Schofield, Nicola T Fear, Christopher J Boos, Paul Cullinan, Eleanor Miller, Owen Walker, & Tass White. We continue to be grateful to those who serve, especially with ADVANCE. Data Availability Data relate to serving and ex-serving military personnel, are sensitive and are not widely available, however, requests for data can be made via the corresponding author and will be considered on a case-by-case basis and subject to UK Ministry of Defence clearance. The code used for analysis will be shared on request to the corresponding author. References Hunter DJ, Bierma-Zeinstra S, Osteoarthritis. Lancet. 2019;393(10182):1745–59. Brown TD, Johnston RC, Saltzman CL, Marsh JL, Buckwalter JA. Posttraumatic osteoarthritis: a first estimate of incidence, prevalence, and burden of disease. J Orthop Trauma. 2006;20(10):739–44. Rivera JC, Wenke JC, Buckwalter JA, Ficke JR, Johnson AE. Posttraumatic Osteoarthritis Caused by Battlefield Injuries: The Primary Source of Disability in Warriors. JAAOS - J Am Acad Orthop Surg. 2012;20:S64–9. Whittaker JL, Losciale JM, Juhl CB, Thorlund JB, Lundberg M, Truong LK, et al. Risk factors for knee osteoarthritis after traumatic knee injury: a systematic review and meta-analysis of randomised controlled trials and cohort studies for the OPTIKNEE Consensus. Br J Sports Med. 2022;56(24):1406–21. von Porat A, Roos EM, Roos H. High prevalence of osteoarthritis 14 years after an anterior cruciate ligament tear in male soccer players: a study of radiographic and patient relevant outcomes. Ann Rheum Dis. 2004;63(3):269–73. Buckwalter JA, Brown TD. Joint injury, repair, and remodeling: roles in post-traumatic osteoarthritis. Clin Orthop Relat Research®. 2004;423:7–16. Vincent TL, editor. Mechanoflammation in osteoarthritis pathogenesis. Seminars in arthritis and rheumatism. 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Palmer D, Cooper D, Whittaker JL, Emery C, Batt ME, Engebretsen L, et al. Prevalence of and factors associated with osteoarthritis and pain in retired Olympians compared with the general population: part 1 – the lower limb. Br J Sports Med. 2022;56(19):1123. Fernandes GS, Parekh SM, Moses J, Fuller C, Scammell B, Batt ME, et al. Prevalence of knee pain, radiographic osteoarthritis and arthroplasty in retired professional footballers compared with men in the general population: a cross-sectional study. Br J Sports Med. 2018;52(10):678. Bennett AN, Dyball DM, Boos CJ, Fear NT, Schofield S, Bull AM, et al. Study protocol for a prospective, longitudinal cohort study investigating the medical and psychosocial outcomes of UK combat casualties from the Afghanistan war: the advance study. BMJ open. 2020;10(10):e037850. Behan FP, Bennett AN, Watson F, Schofield S, O’Sullivan O, Boos CJ, et al. Osteoarthritis after major combat trauma. The Armed Services Trauma Rehabilitation Outcome Study Rheumatology Advances in Practice; 2025. O'Sullivan O, Stocks J, Schofield S, Bilzon J, Boos CJ, Bull AMJ, et al. Association of serum biomarkers with radiographic knee osteoarthritis, knee pain and function in a young, male, trauma-exposed population - findings from the ADVANCE study. Osteoarthritis Cartilage. 2024;32(12):1636–46. Hollis B, Chatzigeorgiou C, Southam L, Hatzikotoulas K, Kluzek S, Williams A, et al. Lifetime risk and genetic predisposition to post-traumatic OA of the knee in the UK Biobank. Osteoarthritis Cartilage. 2023;31(10):1377–87. O’Sullivan O, Watson F, Bull AMJ, Schofield S, Coady EC, Boos CJ et al. , . Influence of Major Trauma and Lower Limb Loss on Radiographic Progression and Incidence of Knee Osteoarthritis and Pain: A Comparative and Predictive Analysis from the ADVANCE Study. Arthritis Research and Therapy. 2025;under review. Altman RD, Gold GE. Atlas of individual radiographic features in osteoarthritis, revised. Osteoarthr Cartil. 2007;15:A1–56. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42(2):377–81. Nehrer S, Ljuhar R, Steindl P, Simon R, Maurer D, Ljuhar D, et al. Automated Knee Osteoarthritis Assessment Increases Physicians' Agreement Rate and Accuracy: Data from the Osteoarthritis Initiative. Cartilage. 2021;13(1suppl):s957–65. Smolle MA, Goetz C, Maurer D, Vielgut I, Novak M, Zier G, et al. Artificial intelligence-based computer-aided system for knee osteoarthritis assessment increases experienced orthopaedic surgeons' agreement rate and accuracy. Knee Surg Sports Traumatol Arthrosc. 2023;31(3):1053–62. Yoong S, Miles D, McKinney P, Smith I, Spencer N. A method of assigning socio-economic status classification to British armed forces personnel. J R Army Med Corps. 1999;145(3):140–2. Office for National Statistics. SOC 2020 Volume 3: the National Statistics Socio-economic Classification (NS-SEC rebased on the SOC 2020) 2021 [Available from: https://www.ons.gov.uk/methodology/classificationsandstandards/standardoccupationalclassificationsoc/soc2020/ soc2020volume3thenationalstatisticssocioeconomicclassificationnssecrebasedonthesoc2020 Bertoli S, Leone A, Krakauer NY, Bedogni G, Vanzulli A, Redaelli VI, et al. Association of Body Shape Index (ABSI) with cardio-metabolic risk factors: A cross-sectional study of 6081 Caucasian adults. PLoS ONE. 2017;12(9):e0185013. Ding Z, Jarvis HL, Bennett AN, Baker R, Bull AMJ. Higher knee contact forces might underlie increased osteoarthritis rates in high functioning amputees: A pilot study. J Orthop Res. 2021;39(4):850–60. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc: Ser B (Methodol). 1995;57(1):289–300. EMA. Guideline on clinical investigation of medicinal products used in the treatment of osteoarthritis. Committee for Medical Products for Human Use, European Medicines Agency; 2010. FDA, Osteoarthritis. Structural Endpoints for the Development of Drugs, Devices, and Biological Products for Treatment Guidance for Industry. Federal Drugs Agency; 2018. Jordan JM. Cartilage oligomeric matrix protein as a marker of osteoarthritis. JOURNAL OF RHEUMATOLOGY-SUPPLEMENT-; 2004. pp. 45–9. Daghestani HN, Kraus VB. Inflammatory biomarkers in osteoarthritis. Osteoarthritis Cartilage. 2015;23(11):1890–6. Felson DT, Gale DR, Elon Gale M, Niu J, Hunter DJ, Goggins J, et al. Osteophytes and progression of knee osteoarthritis. Rheumatology. 2004;44(1):100–4. Sharma L, Song J, Felson DT, Cahue S, Shamiyeh E, Dunlop DD. The role of knee alignment in disease progression and functional decline in knee osteoarthritis. JAMA. 2001;286(2):188–95. Castañeda S, Vicente EF. Osteoarthritis: More than Cartilage Degeneration. Clin Rev Bone Miner Metab. 2017;15(2):69–81. Lajeunesse D, Massicotte F, Pelletier JP, Martel-Pelletier J. Subchondral bone sclerosis in osteoarthritis: not just an innocent bystander. Mod Rheumatol. 2003;13(1):0007–14. Kraus VB, Collins JE, Charles HC, Pieper CF, Whitley L, Losina E, et al. Predictive validity of radiographic trabecular bone texture in knee osteoarthritis: the Osteoarthritis Research Society International/Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheumatol. 2018;70(1):80–7. Niemczyk A, Waśkiel-Burnat A, Zaremba M, Czuwara J, Rudnicka L. The profile of adipokines associated with fibrosis and impaired microcirculation in systemic sclerosis. Adv Med Sci. 2023;68(2):298–305. Scotece M, Conde J, Lopez V, Lago F, Pino J, Gómez-Reino JJ, et al. Adiponectin and leptin: new targets in inflammation. Basic Clin Pharmacol Toxicol. 2014;114(1):97–102. Kraus VB, Reed A, Soderblom EJ, Moseley MA, Hsueh M-F, Attur MG, et al. Serum proteomic panel validated for prediction of knee osteoarthritis progression. Osteoarthr Cartil Open. 2024;6(1):100425. Additional Declarations No competing interests reported. Supplementary Files ARTSupplementaryfiles.docx Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Arthritis Research & Therapy → Version 1 posted Editorial decision: Revision requested 16 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 01 May, 2025 Reviews received at journal 01 Apr, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers invited by journal 10 Mar, 2025 Editor assigned by journal 05 Mar, 2025 Submission checks completed at journal 05 Mar, 2025 First submitted to journal 27 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6120483\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":444376598,\"identity\":\"50dc89a4-4b03-44d1-bf6b-be99e0d34e10\",\"order_by\":0,\"name\":\"Oliver O'Sullivan\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABR0lEQVRIie2RQUvDMBiGE3roRem1Bd3+QqTgaXR/paHgyYrgZeDAlEJ3KXiNCPoXIoIieIgEuoOFXQvusDLYycG87TDBtJuDlU48iuQhkDfke/IlBACF4i+iQ7JKWsBdORnlApVjiVlRtLUCSalYpEg/KuskC8vz+SptUwxNi8yPZ9BoUkx4Hjkn9lvYH89Oh86j/uqCeRfgK7KhWCGMLDoBNsqkgiPv7HCYyIQm3lPsMxgnAF9vdkEChvYOB5iZstBNNfyQHR9IRXiI+wzsEoBvapSFVG5pqVzge/qtDKYMftYqwRhIhRQXcztCtlsqDsp8phVdKheTbwnymJs2SvNC6WOaHWGaIuGibMrEXmLalecbem/E57zVaPa8l3yOzvEl9fissxBtNPDv8vdua59yUEP1tyS4OJvXbm2j/ftShUKh+Od8ARjEiNiBk72CAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"University of Nottingham\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Oliver\",\"middleName\":\"\",\"lastName\":\"O'Sullivan\",\"suffix\":\"\"},{\"id\":444376599,\"identity\":\"d040a3c0-74dc-430e-941a-e07ad31216e1\",\"order_by\":1,\"name\":\"Ana M Valdes\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Nottingham\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ana\",\"middleName\":\"M\",\"lastName\":\"Valdes\",\"suffix\":\"\"},{\"id\":444376600,\"identity\":\"2641c22e-0222-458a-a0f3-b97497bffee9\",\"order_by\":2,\"name\":\"Fraje Watson\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fraje\",\"middleName\":\"\",\"lastName\":\"Watson\",\"suffix\":\"\"},{\"id\":444376601,\"identity\":\"5316063a-2da9-4b50-b3c2-d2a8b79167ad\",\"order_by\":3,\"name\":\"Stefan Kluzek\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Nottingham\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Stefan\",\"middleName\":\"\",\"lastName\":\"Kluzek\",\"suffix\":\"\"},{\"id\":444376602,\"identity\":\"4537f9f2-4c8e-41d2-8896-43b54927b533\",\"order_by\":4,\"name\":\"Anthony M J Bull\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Anthony\",\"middleName\":\"M J\",\"lastName\":\"Bull\",\"suffix\":\"\"},{\"id\":444376603,\"identity\":\"e479ce04-e313-44b2-9671-9d437663b4ae\",\"order_by\":5,\"name\":\"Alexander N Bennett\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Academic Department of Military Rehabilitation, DMRC Stanford Hall\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Alexander\",\"middleName\":\"N\",\"lastName\":\"Bennett\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-02-27 11:23:37\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6120483/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6120483/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s13075-025-03648-y\",\"type\":\"published\",\"date\":\"2025-11-05T15:56:51+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":81012013,\"identity\":\"46c9e719-e6b5-4304-8641-3de353c036b7\",\"added_by\":\"auto\",\"created_at\":\"2025-04-21 08:26:13\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":5863,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCross-sectional correlations between the panel of serum biomarkers and individual radiographic features (joint space narrowing, osteophyte, sclerosis)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eCorrelations are corrected for using the false discovery rate (q-value). All investigations at baseline (8-year) visit\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eIL: Interleukin, TNF: Tumour necrosis factor, CTX-II: C-terminal cross-linked telopeptide of type II collagen, COMP: cartilage oligomeric protein, PIIANP: N-propeptide of collagen IIA, Adipo: Adiponectin. JSN: Joint space narrowing, OP: osteophytes, Scl: Sclerosis.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6120483/v1/5049ea701f6895bec67adba1.png\"},{\"id\":81010523,\"identity\":\"881ae180-75d5-433e-b6d0-292caa357e2c\",\"added_by\":\"auto\",\"created_at\":\"2025-04-21 08:18:13\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":11508,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLongitudinal correlations between the panel of serum biomarkers and new (a) or progression of (b) radiographic features (joint space narrowing, osteophyte, sclerosis)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eCorrelations are corrected for using a false discovery rate (q-value). Serum biomarkers from 8-year visit, radiographs from 11-year visit.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eIL: Interleukin, TNF: Tumour necrosis factor, CTX-II: C-terminal cross-linked telopeptide of type II collagen, COMP: cartilage oligomeric protein, PIIANP: N-propeptide of collagen IIA, Adipo: Adiponectin. JSN: Joint space narrowing, OP: osteophytes, Scl: Sclerosis.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6120483/v1/301c620c639e968965d30091.png\"},{\"id\":95564133,\"identity\":\"b3625d6b-1465-4abd-95a9-e6e1985a7a20\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 16:08:12\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":802905,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6120483/v1/639078f0-0e72-4108-b0b7-a53dfab2346a.pdf\"},{\"id\":81012015,\"identity\":\"131c50e9-bdf0-427e-a47a-6af2ed870b49\",\"added_by\":\"auto\",\"created_at\":\"2025-04-21 08:26:13\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":227297,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ARTSupplementaryfiles.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6120483/v1/8b14628748ba2968c0d03200.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Insights into knee post-traumatic osteoarthritis pathophysiology from the relationship of serum biomarkers to radiographic features in the ADVANCE cohort\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eOsteoarthritis is a complex, multifaceted disease influenced by modifiable and non-modifiable risk factors, resulting in a clinical continuum from a prodromal asymptomatic condition to a highly disabling painful clinical syndrome(\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). It has been estimated that 13% of knee OA can be attributed to previous trauma (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e), with certain injuries, such as anterior cruciate ligament (ACL) rupture, meniscal injury, or intra-articular fracture, carrying a significant risk of subsequent post-traumatic OA (PTOA), presenting within a few years after injury, with a period of intermittent symptomatic recovery prior to symptomatic and functional decline (\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe initial traumatic episode can result in localised disruption of articular cartilage, cartilage fissures, and chondrocyte death, accompanied by a post-traumatic inflammatory response and synovitis, as well as concurrent damage affecting the biomechanical function of the joint, such as fractures (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Immediately after this, multiple signalling pathways activate, resulting in cartilage matrix degradation and synovial inflammation, leading to repair, remodelling, and adaptation. However, aberrant mechanistic pathways can contribute to a lack of repair and remodelling, inadequate adaptation, and the subsequent development of PTOA, with altered biomechanics, metabolism, and low-grade inflammation resulting in disturbed joint homeostasis. Unlike idiopathic OA, where pathological articular cartilage changes trigger negative surrounding structural changes, it is possible that in early PTOA, structural changes influence local bone and cartilage compositional changes (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). These changes involve cartilage matrix macromolecule synthesis or subchondral bone resorption, mediated by cytokines, and leave signals which can be detected at a molecular level (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn recent decades, there has been a research focus on early identification and preventative strategies alongside a renewed focus on drug discovery to slow the impact of this common disabling condition, especially through the use of biological markers (biomarkers), defined by the National Institutes for Health (NIH) as ‘\\u003cem\\u003ea characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention’\\u003c/em\\u003e(\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e). Biomarkers have been categorised by type (type 0: elucidate disease natural history, type 1: measuring intervention effect, type 2: a surrogate endpoint)(\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e), future role (burden of disease, investigative, prognostic, efficacy of intervention, diagnosis or safety, BIPEDS)(\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) and level of quantification (exploration, demonstration, characterisation, surrogacy)(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Multiple biomarker types are under investigation, including molecular (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e), imaging (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) and biomechanical (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e), which are compared to either structural change, through the use of x-ray, including Kellgren-Lawrence (KL) grades (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e), or pain and symptoms, through the use of validated patient-reported outcome measures (PROMs), such as the Knee injury and OA Outcome Score (KOOS)(\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). Each of these modalities offers a different insight into PTOA pathophysiology and clinical picture; however, challenges remain within each modality, including a clear understanding of how the observed metric relates to the underlying disease process.\\u003c/p\\u003e \\u003cp\\u003eCertain molecular biomarkers relate to future pain or disease progression, with academic-regulatory-pharmacological cross-sector coalescence around the most promising biomarkers to improve their validation and qualification spearheaded by the NIH (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). Eight molecular biomarkers significantly predicted case status, with three (urinary C-terminal cross-linked telopeptide of type II collagen, CTX-II, serum hyaluronan (HA), serum crosslinked N-telopeptide of type I collagen, NTX-1) offering the best prediction of future OA (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Phase 2 of this work, the OA PROGRESS study, is nearing completion, with the ambition to enable the validation of biochemical (as well as imaging) measures for prognosis and prediction of disease progression over 48 months using thousands of pooled participant data from existing trial data(\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). However, the vast majority of those included in these pooled data are aged ≥ 60 years old (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e), and whilst OA does become more common with age, it can commence and present during any stage of adult life, especially in those with a traumatic cause.\\u003c/p\\u003e \\u003cp\\u003eIn nearly 40% of those with OA, symptoms commenced before 45-years-old, with those experiencing symptoms prior to 35 years old potentially waiting a decade before receiving a diagnosis(\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). Furthermore, certain occupational groups, such as physical jobs, sportspeople and emergency services, are associated with an increased risk of PTOA with increased surveillance and intervention required to mitigate this(\\u003cspan additionalcitationids=\\\"CR23\\\" citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). Additionally, after exposure to major trauma, PTOA can present far sooner, in less than two years(\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). The Armed Services Trauma Rehabilitation Outcome (ADVANCE) study is a prospective, longitudinal cohort study which aims to understand the long-term physical and psycho-social effects of combat injury in 1145 British servicemen, half of whom were seriously injured during the Afghanistan war. The ADVANCE study offers, a young, heterogenous and high-risk cohort in which to investigate PTOA further to understand the relationship between molecular biomarkers and pathophysiology(\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eEarlier work demonstrated increased rates of knee OA across the cohort, up to 4x in those with combat injury or lower-limb loss(\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e), but also nearly 20% of the non-injured comparison British servicemen(\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). Further serum biomarker analysis revealed adipokines were able to classify those experiencing pain. Although serum biomarker analysis were not able to differentiate those with and without OA, it did identified that there was no molecular difference between those with OA in the exposed group (PTOA) and in the unexposed group (early-onset idiopathic OA, iOA)(\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). We speculate that the inability of the serum biomarkers to differentiate OA cases was, in part, due to the predominance of early changes (70% of all cases were KL grade 1), and that the molecular similarities between post-traumatic and idiopathic aetiologies were due to the length of time since injury. Other cohorts have demonstrated an increased post-injury risk, plateauing as time from injury increases (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e), a hypothesis supported in the ADVANCE cohort by no increased rates of radiographic OA between exposed and unexposed groups between the baseline visit (8 years from injury) and the first follow-up visit three years later (11 years from injury) (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTherefore, the ADVANCE study offers an exciting opportunity to further explore the pathophysiological processes of OA present in a younger cohort, using the individual components of the OARSI radiological atlas (joint space narrowing (JSN), osteophytes, sclerosis)(\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e) and panel of extracellular matrix turnover, inflammatory and metabolic serum biomarkers, using data from the 8-year and recently completed 11-year from major trauma or matched deployment visits. This unique cohort will enable further analysis to elucidate the natural course of the disease, investigate and offer prognostic ability, and add to the evidence base for the further quantification of established serum biomarkers.\\u003c/p\\u003e \\u003cp\\u003eThe hypothesis of this study is that serum biomarkers measuring different pathophysiological processes will be associated and predictive of different radiological features of PTOA. The aim is two-fold; firstly, to undertake a cross-sectional analysis of the panel of candidate serum biomarkers to OARSI radiographic atlas features (JSN, osteophytes, sclerosis); and secondly, to undertake a longitudinal analysis to ascertain the ability of these biomarkers to predict individual radiological feature development or progression over three years.\\u003c/p\\u003e \\n\\n \"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eEthics\\u003c/p\\u003e\\u003cp\\u003e Favourable opinion for ADVANCE was granted by MoD Research Ethics Committee (MODREC:357PPE12) with subsequent approval from the University of Nottingham Faculty of Medicine and Health Sciences REC (UoN FMHS 170–1122). Study participation is voluntary, with written informed consent from each participant at each study visit, with the study performed in accordance with the Declaration of Helsinki.\\u003c/p\\u003e\\u003ch3\\u003ePPI\\u003c/h3\\u003e\\u003cp\\u003ePublic and Patient Involvement is undertaken using a variety of methods, including face-to-face and remote participant panels, feedback questionnaires for each study visit, newsletters, participant-focussed impact reports, and the study website (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ewww.advancestudydmrc.org.uk\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.advancestudydmrc.org.uk\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). These have helped refine study design, logistics and tailor further research questions relevant to the interests of the participants.\\u003c/p\\u003e\\u003cp\\u003eParticipants\\u003c/p\\u003e\\u003cp\\u003eADVANCE is a longitudinal cohort study monitoring the long-term physical and psychosocial outcomes following combat injury in UK military personnel who served in Afghanistan, involving 579 participants who sustained combat injuries requiring aeromedical evacuation to the UK (Exposed), and 566 participants not exposed to combat injury who were frequency-matched for age, rank, role, service, and deployment (Unexposed). Female military personnel are excluded due to the number sustaining combat injuries being too low to generate sufficient statistical power. Additional information on recruitment and inclusion/exclusion criteria can be found in the protocol paper and previously published papers(\\u003cspan additionalcitationids=\\\"CR26\\\" citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). Potential exposed participants were identified using military health records, with frequency matched unexposed comparison participants identified by the Defence Statistics (Health) team.\\u003c/p\\u003e\\u003cp\\u003eData collection\\u003c/p\\u003e\\u003cp\\u003eStudy visits occurred at the Defence Medical Rehabilitation Centre Headley Court (2015–2018) or Stanford Hall (2018–2024) over the period of one day, with participants fasted and absent from caffeine and alcohol for at least 8 hours before the visit. Trained research nurses collect a range of assessments, including sociodemographic, anthropometric, medical history (including existing medical problems, combat trauma, and significant family history), serum sampling, and radiographs of their knees. Study data were collected and managed using Research Electronic Data Capture (REDCap), a secure, web-based software platform(\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e). This study reports data collected at the baseline (8-years post-injury/deployment) and first follow-up (11-years post-injury/deployment visit of the ADVANCE study.\\u003c/p\\u003e\\u003cp\\u003eRadiography\\u003c/p\\u003e\\u003cp\\u003eSemi-flexed (7–10°) posterior-anterior views of all possible participant knees were taken using a Synaflexer X-ray positioning frame (Synarc Inc, San Francisco, California). The tibiofemoral joint was scored using the Osteoarthritis Research Symposium International (OARSI) atlas(\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e), using KOALA with manual checking, which grades JSN, sclerosis and osteophytes using a 4-point scale (0 – none, 1 – mild, 2 – moderate, 3 – severe). No member of the radiographic reporting team was aware of the participant’s clinical status. The FDA-approved Knee OA Labelling Assistant (KOALA, Image Biopsy Lab, Vienna, Austria) with manual checking scored the x-ray, offering an accuracy of 76%, 84%, 60%, sensitivity of 97%, 88%, 97% and specificity of 41%, 73% and 24% for all grades JSN, osteophyte and sclerosis, rising to 84% and 88%, 71% and 77%, 94% and 92%, respectively, in the presence of osteophytes and sclerosis (≥ 1)(\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). The aided AI KOALA tool improves reader agreement rates by 1.37, 1.59, and 1.42-fold when assessing JSN, osteophyte or sclerosis grade (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAnalyses were performed using the presence of any individual measures of the OARSI atlas (JSN, osteophytes, sclerosis) to ascertain if any specific biomarkers offered improved identification of different pathological mechanisms in the cartilage, surface or subchondral bone matrix. When participants had two variables (from both knees), the index knee score signifying more advanced rOA was selected (higher JSN/osteophyte/sclerosis grade). Progression was defined as an increase in individual radiographic feature (JSN/osteophyte/sclerosis) by ≥ 1 at Follow-up in a knee with at least JSN/osteophyte/sclerosis ≥ 1 at Baseline. Incidence was defined as the presence of a radiographic feature of knee OA (JSN/osteophyte/sclerosis ≥ 1) at Follow-up in a knee that was JSN/osteophyte/sclerosis 0 at Baseline. Each feature was taken in turn and analysed separately to understand the relationship between the serum biomarker and the possible pathophysiological change reflected by the radiographic feature.\\u003c/p\\u003e\\u003cp\\u003eBiomarker analysis\\u003c/p\\u003e\\u003cp\\u003eParticipants underwent serum sampling with fasted blood taken from the antecubital fossa using the Vacutainer system. It was centrifuged at 3500rpm for 10 minutes, and then aliquoted to be stored into cryovials and stored in monitored freezers at -80°. Frozen baseline samples were analysed by Affinity Biomarker Laboratory (ABL), London, for cartilage oligomeric protein (COMP), CTX-II, N-propeptide of collagen IIA (PIIANP), interleukin (IL)-1b, IL-17a, tumour necrosis factor (TNF)-a, leptin and adiponectin via Meso Scale Discovery (MSD) or enzyme-linked immunosorbent assay (ELISA). This panel of serum candidate biomarkers, only sampled at baseline, were chosen to examine different pathological mechanisms, including aberrant tissue turnover, inflammatory dysfunction and metabolic dysregulation.\\u003c/p\\u003e\\u003cp\\u003eResults underwent quality assurance and control by ABL, who were also not aware of the participants clinical status, using three internally identified quality control samples and two kit controls. The worst reported intra- or inter-variability coefficient of variation for each biomarker were; MSD: IL-17a \\u0026lt; 9.5%, IL-1β \\u0026lt; 7%, TNF-a \\u0026lt; 15%; ELISA: COMP \\u0026lt; 12%, leptin \\u0026lt; 7%, adiponectin \\u0026lt; 8%, CTX-II \\u0026lt; 11%, PIIANP \\u0026lt; 6%. Any biomarker concentration level below the lower limit of quantification (LLOQ) was given a value halfway between zero and the LLOQ threshold, with those above the upper LOQ (ULOQ) given ULOQ threshold + 1, required for IL-17a (\\u0026lt; 0.54 = 0.27, n = 24), PIIANP (\\u0026lt; 5.9 = 2.95 n = 23, \\u0026gt; 1000 = 1001, n = 9), CTX-II (\\u0026lt; 0.1 = 0.05, n = 421), and IL-1β (\\u0026lt; 0.043 = 0.0215, n = 713).\\u003c/p\\u003e\\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\u003cp\\u003e All data were screened for normality visually using histograms, with parametric and non-parametric testing used accordingly and presented as mean (standard deviation, SD) and median (interquartile range, IQR), respectively. As a result of earlier work, demonstrating no molecular differences between those with OA in the exposed and non-exposed groups, participants are dichotomised due to presence of individual radiographic OA feature, not exposure status. The analysis was performed in two parts, to address each aim in turns.\\u003c/p\\u003e\\u003cp\\u003eFirst, an initial descriptive analysis was performed to identify presence and amount of individual OA features, with correlation analysis performed with biomarkers standardised to a mean = 0 and SD = 1 to visualise patterns in the data (Spearman’s or Pearson’s, full results in Supplementary File 1). Univariate analysis was subsequently performed, depending on normality and the presence or absence of each individual radiographic feature (Wilcoxon rank sum and Student’s t test). Unadjusted analyses were initially performed using the baseline data, followed by adjusted, with the confounders age, body mass, time from injury/deployment, exposure to trauma, military rank (as a proxy for socio-economic status, SES(\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e)) and ethnicity adjusted for. This was performed by transforming the biomarkers using their natural logarithm and adjusted for the confounders using a regression model, with studentised residuals created and taken forward for analysis. Within an athletic population, the body mass index (BMI) can ‘overscore’ individuals with a high muscle mass; therefore, a body shape index (ABSI)(\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e), calculated with BMI and waist circumference, was utilised, which gives a balanced reflection of body weight and central adiposity. Time from injury/deployment was measured from the participant’s index deployment.\\u003c/p\\u003e\\u003cp\\u003eNext, using longitudinal data from the ADVANCE 11-year follow up visit, an analysis of the predictive value of the serum biomarkers was performed. Participant x-rays were assessed and dichotomised for either the incidence or progression of each individual radiographic feature as described above, with correlation analysis again used with standardised biomarkers to visualise patterns (full results, Supplementary File 1). Due to the numbers of participants within each of these groups, a least absolute shrinkage and selection operator (LASSO) variable selection model was performed to identify significant relationships between serum biomarkers and radiographic features. When significant predictors were identified, multivariate logistic regression was performed, with Nagelkerke’s R2 and area under the receiver operator curve (AUROC) reported (further regression results in Supplementary File 2). Due to the small numbers in groups, full adjustment was not possible, with only unadjusted results reported.\\u003c/p\\u003e\\u003cp\\u003eThe ADVANCE study recruited n = 1145 male British servicemen (exposed n = 579, unexposed n = 566) at baseline, with n = 1052 attending the first follow-up visit (92%). A whole case approach was adopted for this analysis, therefore, the n = 190 participants who did not have x-ray at both time points or biomarker data available were excluded, inclusive of those who did not attend follow-up (n = 93). In addition, to prevent confounding due to the likely different pathological processes at play for those sustaining lower-limb loss and subsequent OA(\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e), the n = 161 individuals sustaining amputations were also excluded, leaving 872 participants.\\u003c/p\\u003e\\u003cp\\u003eGiven the large number of comparisons performed in this exploratory study, results have been additionally corrected using the Benjamini and Hockberg method, with results corrected using the false discovery rate (q-value)(\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e), with the significance rate set at 0.1. Data is presented with the unadjusted, uncorrected p-value and the fully adjusted, corrected q-value. Presenting data both in its unadjusted and adjusted forms allows other studies to compare results and enables the effect of OA to be more accurately partitioned. Analyses were performed in Stata 18.5 (StataCorp LLC, Texas) and GraphPad Prism 10 (Dotmatics, Boston).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eEight-hundred and seventy-two male participants, aged 34.5 (5.5) at the 8-year and 38.3 (5.4) at the 11-year visit are included, at a mean 9.0 (2.2) years from injury (for the 42% of injured participants) or index deployment at baseline and 3.3 years (0.6) between baseline and follow-up (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eParticipant demographics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal (n\\u0026thinsp;=\\u0026thinsp;872)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, yrs (BL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34.5 (5.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, yrs (FU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38.3 (5.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e (BL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27.6 (3.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e (FU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28.2 (3.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAbdo. circum., cm (BL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e93.0 (87.5-100.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAbdo. circum., cm (FU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e96.0 (90.0-103.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExposed, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e364 (41.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMilitary Rank, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJunior NCO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e542 (62%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSenior NCO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e211 (24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOfficer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e119 (14%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTime from injury, yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.0 (2.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTime to follow-up, yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.3 (0.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaucasian, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e771 (88.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003e\\u003cem\\u003eBL: Baseline, FU: Follow up, yrs: years, BMI: body mass index, kg: kilograms, M: metres, abdo circum: abdominal circumference, cm \\u0026ndash; centimetres, NCO \\u0026ndash; non-commissioned officer. Data presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation, median (interquartile range) or n= (%)\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt baseline, n\\u0026thinsp;=\\u0026thinsp;331 (38%) participants had JSN, n\\u0026thinsp;=\\u0026thinsp;165 (19%) had osteophytes and n\\u0026thinsp;=\\u0026thinsp;74 (9%) sclerosis evident on their knee radiographs, rising to n\\u0026thinsp;=\\u0026thinsp;370 (42%), n\\u0026thinsp;=\\u0026thinsp;233 (27%) and n\\u0026thinsp;=\\u0026thinsp;117 (13%) by follow-up, respectively (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Across all categories, the vast majority were Grade 1 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), with a mild increase in severity between study visits, with 3% of JSN, 10% of osteophyte and 16% of sclerosis cases increasing by \\u0026ge;\\u0026thinsp;1 grade (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). In individuals without specific radiological change at the 8-year visit, 19% developed new JSN, 15% developed new osteophytes and 9% developed new sclerosis by the 11-year visit (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eRates of joint space narrowing (JSN), osteophytes and sclerosis in the ADVANCE cohort at baseline and follow-up\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eFollow-up\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJSN, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e331 (38.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eJSN, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e370 (42.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJSN grade, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eJSN grade, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e541 (62%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e502 (58%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e307 (35%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e343 (39%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOsteophytes, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e165 (18.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOsteophytes, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e233 (26.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOP grade, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOP grade, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e707 (81%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e639 (73%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e132 (15%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e191 (22%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23 (3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSclerosis, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74 (8.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSclerosis, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e117 (13.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eScl grade, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eScl grade, n= (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e798 (92%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e755 (87%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e64 (7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e98 (11%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10 (1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eJSN: Joint space narrowing, Scl: Sclerosis, OP: Osteophytes. Data presented as n= (%)\\u003c/h3\\u003e\\n\\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eRates of new or progressive joint space narrowing (JSN), osteophytes and sclerosis between baseline and follow-up\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eJSN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eOsteophytes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eSclerosis\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNew\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eProgress\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNew\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eProgress\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNew\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eProgress\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e105/541 (19%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10/331\\u003c/p\\u003e \\u003cp\\u003e(3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e104/707\\u003c/p\\u003e \\u003cp\\u003e(15%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17/165\\u003c/p\\u003e \\u003cp\\u003e(10%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e69/798\\u003c/p\\u003e \\u003cp\\u003e(9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e12/74\\u003c/p\\u003e \\u003cp\\u003e(16%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e shows the cross-sectional correlation between the panel of serum biomarkers and individual radiographic features performed at the 8-year visit (full results, Supplementary File 1). Those with JSN changes at baseline had significantly higher levels of COMP (p\\u0026thinsp;=\\u0026thinsp;0.002) and leptin (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). After adjustment and correction, the difference in leptin remained significant (q\\u0026thinsp;=\\u0026thinsp;0.04). COMP (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and leptin (p\\u0026thinsp;=\\u0026thinsp;0.006) were significantly higher, and PIIANP significantly lower (p\\u0026thinsp;=\\u0026thinsp;0.009) between those with and without osteophytes at baseline; however, after adjustment and correction, no differences remained significant (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). In those with sclerosis at baseline, leptin was higher (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with PIIANP (p\\u0026thinsp;=\\u0026thinsp;0.036) and adiponectin (p\\u0026thinsp;=\\u0026thinsp;0.015) both significantly lower than those without (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). After adjustment and correction, neither remained significant.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows the longitudinal correlation between the panel of serum biomarkers and the new or progressive cases of the individual radiographic features (full results, SF1). LASSO selected COMP as a predictor for new cases of JSN, with an area under the receiver curve (AUROC) of 0.604 (95% confidence interval, CI, 0.543,0.664) and R2 0.018 (Supplementary File 2). COMP, IL-1β and leptin were selected as predictors of new osteophyte cases, with an AUROC\\u0026thinsp;=\\u0026thinsp;0.586 (95% CI 0.525,0.646), R2 0.018 (Supplementary File 2). TNF-α, IL-1β and adiponectin were selected as predictors for new cases of sclerosis, AUROC\\u0026thinsp;=\\u0026thinsp;0.590 (95% CI 0.520,0.659), R2 0.022 (Supplementary File 2). No biomarkers were selected by LASSO for the prediction of JSN, osteophyte or sclerosis progression.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDifferences in serum biomarker concentration between those with and without joint space narrowing, osteophytes and sclerosis within the ADVANCE cohort\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"11\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo JSN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eJSN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003cp\\u003eq-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNo OP\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eOP\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003cp\\u003eq-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eNo Scl\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eScl\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003cp\\u003eq-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;872\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;541\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;331\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;707\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;165\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;798\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIL-1β\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003cp\\u003e(0.02\\u0026ndash;0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003cp\\u003e(0.02\\u0026ndash;0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003cp\\u003e(0.02\\u0026ndash;0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.713~\\u003c/p\\u003e \\u003cp\\u003e0.811~\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003cp\\u003e(0.02\\u0026ndash;0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003cp\\u003e(0.02\\u0026ndash;0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.340~\\u003c/p\\u003e \\u003cp\\u003e0.402~\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003cp\\u003e(0.02\\u0026ndash;0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003cp\\u003e(0.02\\u0026ndash;0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.490~\\u003c/p\\u003e \\u003cp\\u003e0.678~\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTNF-α\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.94\\u003c/p\\u003e \\u003cp\\u003e(0.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.92\\u003c/p\\u003e \\u003cp\\u003e(0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.97\\u003c/p\\u003e \\u003cp\\u003e(0.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.280#\\u003c/p\\u003e \\u003cp\\u003e0.527#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.95\\u003c/p\\u003e \\u003cp\\u003e(0.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.92\\u003c/p\\u003e \\u003cp\\u003e(0.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.640#\\u003c/p\\u003e \\u003cp\\u003e0.959#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.94\\u003c/p\\u003e \\u003cp\\u003e(0.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1.94\\u003c/p\\u003e \\u003cp\\u003e(0.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.980#\\u003c/p\\u003e \\u003cp\\u003e0.825#\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIL17-α\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.27\\u003c/p\\u003e \\u003cp\\u003e(0.97\\u0026ndash;1.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.26\\u003c/p\\u003e \\u003cp\\u003e(0.96\\u0026ndash;1.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.31\\u003c/p\\u003e \\u003cp\\u003e(0.97\\u0026ndash;1.87)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.210~\\u003c/p\\u003e \\u003cp\\u003e0.512#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.27\\u003c/p\\u003e \\u003cp\\u003e(0.96\\u0026ndash;1.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.30\\u003c/p\\u003e \\u003cp\\u003e(1.00-1.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.400~\\u003c/p\\u003e \\u003cp\\u003e0.690#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.27\\u003c/p\\u003e \\u003cp\\u003e(0.96\\u0026ndash;1.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1.38\\u003c/p\\u003e \\u003cp\\u003e(1.09\\u0026ndash;1.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.106~\\u003c/p\\u003e \\u003cp\\u003e0.794#\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCTX-II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.21\\u003c/p\\u003e \\u003cp\\u003e(0.05\\u0026ndash;0.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.20\\u003c/p\\u003e \\u003cp\\u003e(0.05\\u0026ndash;0.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.21\\u003c/p\\u003e \\u003cp\\u003e(0.05\\u0026ndash;0.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.700~\\u003c/p\\u003e \\u003cp\\u003e0.512~\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.21\\u003c/p\\u003e \\u003cp\\u003e(0.05\\u0026ndash;0.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.20\\u003c/p\\u003e \\u003cp\\u003e(0.05\\u0026ndash;0.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.450~\\u003c/p\\u003e \\u003cp\\u003e0.690~\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.20 (0.05\\u0026ndash;0.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.32\\u003c/p\\u003e \\u003cp\\u003e(0.05\\u0026ndash;0.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.390~\\u003c/p\\u003e \\u003cp\\u003e0.828~\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeptin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.59\\u003c/p\\u003e \\u003cp\\u003e(2.99\\u0026ndash;9.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.08\\u003c/p\\u003e \\u003cp\\u003e(2.52\\u0026ndash;8.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.40\\u003c/p\\u003e \\u003cp\\u003e(3.59\\u0026ndash;9.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001~\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cb\\u003e0.04~\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5.36\\u003c/p\\u003e \\u003cp\\u003e(2.88\\u0026ndash;8.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e6.26\\u003c/p\\u003e \\u003cp\\u003e(4.00-10.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.006~\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e0.402~\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e5.44\\u003c/p\\u003e \\u003cp\\u003e(2.88\\u0026ndash;8.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e7.02\\u003c/p\\u003e \\u003cp\\u003e(4.31\\u0026ndash;11.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001~\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e0.108~\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCOMP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e273.90\\u003c/p\\u003e \\u003cp\\u003e(87.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e266.77\\u003c/p\\u003e \\u003cp\\u003e(85.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e285.55\\u003c/p\\u003e \\u003cp\\u003e(90.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002#\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e0.512#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e268.77\\u003c/p\\u003e \\u003cp\\u003e(85.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e295.87\\u003c/p\\u003e \\u003cp\\u003e(93.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001#\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e0.402#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e273.02\\u003c/p\\u003e \\u003cp\\u003e(88.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e283.40\\u003c/p\\u003e \\u003cp\\u003e(84.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.330#\\u003c/p\\u003e \\u003cp\\u003e0.825#\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAdipo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.43\\u003c/p\\u003e \\u003cp\\u003e(4.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.54\\u003c/p\\u003e \\u003cp\\u003e(4.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.27\\u003c/p\\u003e \\u003cp\\u003e(4.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.400#\\u003c/p\\u003e \\u003cp\\u003e0.512#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.56\\u003c/p\\u003e \\u003cp\\u003e(4.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5.90\\u003c/p\\u003e \\u003cp\\u003e(4.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.098#\\u003c/p\\u003e \\u003cp\\u003e0.402#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e6.55\\u003c/p\\u003e \\u003cp\\u003e(4.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e5.19\\u003c/p\\u003e \\u003cp\\u003e(2.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.015#\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e0.108#\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePIIANP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e110.50\\u003c/p\\u003e \\u003cp\\u003e(74.30-163.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e114.20\\u003c/p\\u003e \\u003cp\\u003e(74.90-167.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e103.90\\u003c/p\\u003e \\u003cp\\u003e(73.70-159.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.190~\\u003c/p\\u003e \\u003cp\\u003e0.977#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e113.30\\u003c/p\\u003e \\u003cp\\u003e(78.40-167.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e97.20\\u003c/p\\u003e \\u003cp\\u003e(65.20-151.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.009~\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e0.402#\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e111.75\\u003c/p\\u003e \\u003cp\\u003e(75.30-164.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e91.60\\u003c/p\\u003e \\u003cp\\u003e(64.80-153.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.036~\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e0.678#\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eIL: Interleukin, TNF: Tumour necrosis factor, CTX-II: C-terminal cross-linked telopeptide of type II collagen, COMP: cartilage oligomeric protein, PIIANP: N-propeptide of collagen IIA, Adipo: Adiponectin. JSN: Joint space narrowing, OP: osteophytes, Scl: Sclerosis. ~ Wilcoxon rank sum test, # Student\\u0026rsquo;s t-test.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eUnadjusted p-value and adjusted, corrected q-value (for age, body mass, time from injury/deployment, exposure to trauma exposure, socio-economic status and ethnicity, and multiple testing using false discovery rate) Significant results highlighted in bold.\\u003c/em\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis large unique cohort offers insights into the pathophysiological processes leading to the radiographic presentation of PTOA after severe trauma. The study shows that different markers, reflective of different molecular processes, influence the post-traumatic onset of the three key radiographic features of OA, namely JSN, osteophytes and sclerosis. Cross-sectionally, leptin was significantly higher in those with JSN. Longitudinally, COMP offered some predictive value for the new development of JSN, with panels of COMP, IL-1β and leptin, and TNF-α, IL-1β and adiponectin doing the same for new osteophyte and sclerosis, respectively. These differing biomarkers open a window into the underlying pathomechanisms contributing to the early PTOA structural changes that occur after trauma.\\u003c/p\\u003e \\u003cp\\u003ePTOA is of particular interest to researchers, due to presentation in a younger population, with fewer co-morbidities, and a clear initiating event. PTOA pathological processes likely represent a failure of initial injury repair and/or remodelling, and altered joint homeostasis with a resultant imbalance between anabolism and catabolism. It has an accelerated pathophysiological process, present within a few years(\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e), potentially accelerated further following major trauma (such as in military personnel)(\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). As a result, military individuals who have sustained significant injuries during combat are extremely high-risk, and are useful to study from a PTOA mechanistic point of view. Investigating individual radiological features can provide possible explanations of each pathway and therefore, an individual\\u0026rsquo;s underlying endotype. A better understanding of endotypes, just like phenotypes, allows a personalised approach for interventions or improvements in drug discovery trial recruitment(\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRecommended as a primary endpoint for structural change by the European Medicines Agency and the Federal Drugs Agency (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e), JSN is considered a proxy measure for depth, health, and integrity of hyaline articular cartilage. During the PTOA development, articular cartilage undergoes loss in tensile strength and pressure absorption, likely as a result of a mismatch between ECM catabolism and anabolism, and subsequent disruption of homeostasis, in the context of chronic inflammation(\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). This study supports those findings with COMP seen to have an AUROC of 0.604 for new cases of JSN, and whilst the R2 was very low, as were the correlations seen in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, the increase in COMP suggests increased, and unbalanced, catabolic action(\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e). In addition, leptin is higher in those with JSN, an adipokine known to influence OA via an inflammatory mechanism influencing cartilage catabolism further(\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOsteophytes, initially cartilaginous outgrowths which subsequently undergo bony ossification, can be an early sign of OA development, and are created in response to load to increase joint stability and compensate for physiological demand (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e). Joint malalignment, either due to biomechanical influence, co-existing disease, or joint injury, can be initially mitigated by osteophytes, commonly at joint margins, before pathological adaption occurs often due to increased instability or increased load (including body mass)(\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e). The increased COMP and PIIANP levels for those with osteophytes at baseline support the role of ECM turnover in this process, though subsequent adjustment and correction made these differences non-significant. Three biomarkers, COMP, IL-1β and leptin, offered an AUROC of 0.586, with this finding, and the correlations in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, further demonstrating the imbalance of cartilage and collagen catabolism and anabolism in the presence of inflammation.\\u003c/p\\u003e \\u003cp\\u003eSubchondral bone sclerosis can be overlooked in the PTOA process, especially given the focus on understanding the processes underpinning and driving articular cartilage degradation(\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e). The proinflammatory cascade, triggered by chondrocytes in response to cartilage damage, can lead to subchondral bone turnover and increased vascularisation, ending with bone marrow lesions and sclerosis(\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e). However, subchondral bone itself can drive cartilage degeneration, with bone-generated cytokines (including IL-1β and-6) and growth factors (such as insulin-like growth factor-1), passing through the tidemark and driving cartilage metabolism(\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e), hence trabecular bone modelling is a validated marker of PTOA progression(\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e). In addition, adipokines have been seen to be associated with remodelling in OA, and the formation of fibrosis in other conditions(\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e), with post-traumatic fibrosis likely to explain this finding. This study found that the unadjusted adipokines, leptin and adiponectin were significantly higher and lower, respectively in those with sclerosis (non-significant post-adjustment/correction), with TNF-α, IL-1β and adiponectin offering an AUROC of 0.59 for new cases, providing an molecular insight into trabecular and subchondral bone remodelling\\u003c/p\\u003e \\u003cp\\u003eIt is important to contextualise these results. Given the R2 and correlation values, there is likely limited clinical applicability. However, what is notable is that these findings are all in a cohort in their 30\\u0026rsquo;s, male, and with a predominance for early radiographic change, thus, these data offer novel insights into the complex PTOA pathophysiology. It is expected that, as the cohort progresses, the rates and severity will increase further (as has begun between baseline and follow-up, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), offering further insight into PTOA processes. These initial results demonstrate that there are distinct pathophysiological processes at play for each component of PTOA structural change, hence different interventions, especially pharmacological ones, might be required for distinct structural endotypes, and subsequent phenotypes(\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). Given the significant risk of PTOA, especially in those exposed to major trauma, then proactive interventions should be considered, and now different pathological processes have been identified using biomarker clusters, then these pathways should be considered for targeted multi-modal intervention. These results offer a foundation of understanding, which need to be better demarcated. This will be possible as more follow-up visits are completed in this large, unique cohort, with advanced analysis techniques, such as proteomic analysis to better understand underlying pathological processes(\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThere are strengths and limitations to this study. A key strength is the size of population (n\\u0026thinsp;=\\u0026thinsp;872), and whilst the all-male study aged in their 30\\u0026rsquo;s is a limitation, this is an under-represented population in OA research. Another strength of this work is the longitudinal nature of the data. Weaknesses include a lack of a replication cohort, the single radiographic view of the knee, which might contribute to underscoring of OA features, and the floor and ceiling effect of the serum biomarkers, which is likely to reduce the statistical power to detect stronger correlations with radiographic features.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study reports the relationship between a panel of candidate ECM turnover, inflammatory and metabolic serum biomarkers and the individual radiographic features of knee OA in a large, young, male cohort over multiple timepoints. In line with the prespecified hypothesis, different clusters of biomarkers had relationships with each feature, suggesting different pathological processes at play, including predominant unbalanced ECM-catabolism in the presence of inflammation contributing to JSN, ECM-catabolism and increased inflammation contributing to osteophyte development and an inflammation-predominant process contributing to subchondral sclerosis.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFavourable opinion for ADVANCE was granted by MoD Research Ethics Committee (MODREC:357PPE12) with subsequent approval from the University of Nottingham Faculty of Medicine and Health Sciences REC (UoN FMHS 170\\u0026ndash;1122). Study participation is voluntary, with written informed consent from each participant at each study visit, with the study performed in accordance with the Declaration of Helsinki.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll study participants consented to publication of their anonymised data.\\u003c/p\\u003e\\n\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\n\\u003cp\\u003eThe ADVANCE Study is funded through the ADVANCE Charity. Key contributors to the charity are the Headley Court Charity (principal funder), HM Treasury (LIBOR Grant), Help for Heroes, Nuffield Trust for the Forces of the Crown, Forces in Mind Trust, National Lottery Community Fund, Blesma \\u0026ndash; The Limbless Veterans, and the UK Ministry of Defence. Additional funding for this work was provided by Versus Arthritis (21076), and the UK Ministry of Defence (2122.030). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript.\\u003c/p\\u003e\\n\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\n\\u003cp\\u003eAMJB and ANB conceived the study. OOS and ANB gathered data. OOS analysed the data with support from AMV and FW. OOS drafted the manuscript, with support from FW and critical input from all authors. SK, AMV, ANB and AMJB offered expert insight and senior guidance, with OOS, SK, ANB, AMJB involved in acquisition of funding. All authors agreed the final version and are accountable for accuracy and integrity. OOS acts as the guarantor and corresponding author.\\u003c/p\\u003e\\n\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\n\\u003cp\\u003eWe wish to thank all of the research staff at both Headley Court and Stanford Hall who helped with the ADVANCE study, including Emma Coady, Susie Schofield, Nicola T Fear, Christopher J Boos, Paul Cullinan, Eleanor Miller, Owen Walker, \\u0026amp; Tass White. We continue to be grateful to those who serve, especially with ADVANCE.\\u003c/p\\u003e\\n\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\n\\u003cp\\u003eData relate to serving and ex-serving military personnel, are sensitive and are not widely available, however, requests for data can be made via the corresponding author and will be considered on a case-by-case basis and subject to UK Ministry of Defence clearance. The code used for analysis will be shared on request to the corresponding author.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eHunter DJ, Bierma-Zeinstra S, Osteoarthritis. Lancet. 2019;393(10182):1745\\u0026ndash;59.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBrown TD, Johnston RC, Saltzman CL, Marsh JL, Buckwalter JA. Posttraumatic osteoarthritis: a first estimate of incidence, prevalence, and burden of disease. J Orthop Trauma. 2006;20(10):739\\u0026ndash;44.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRivera JC, Wenke JC, Buckwalter JA, Ficke JR, Johnson AE. Posttraumatic Osteoarthritis Caused by Battlefield Injuries: The Primary Source of Disability in Warriors. JAAOS - J Am Acad Orthop Surg. 2012;20:S64\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWhittaker JL, Losciale JM, Juhl CB, Thorlund JB, Lundberg M, Truong LK, et al. Risk factors for knee osteoarthritis after traumatic knee injury: a systematic review and meta-analysis of randomised controlled trials and cohort studies for the OPTIKNEE Consensus. Br J Sports Med. 2022;56(24):1406\\u0026ndash;21.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003evon Porat A, Roos EM, Roos H. High prevalence of osteoarthritis 14 years after an anterior cruciate ligament tear in male soccer players: a study of radiographic and patient relevant outcomes. Ann Rheum Dis. 2004;63(3):269\\u0026ndash;73.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBuckwalter JA, Brown TD. Joint injury, repair, and remodeling: roles in post-traumatic osteoarthritis. Clin Orthop Relat Research\\u0026reg;. 2004;423:7\\u0026ndash;16.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVincent TL, editor. Mechanoflammation in osteoarthritis pathogenesis. Seminars in arthritis and rheumatism. Elsevier; 2019.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eO'Sullivan O, Ladlow P, Steiner K, Kuyser D, Ali O, Stocks J, et al. Knee MRI biomarkers associated with structural, functional and symptomatic changes at least a year from ACL injury - A systematic review. Osteoarthr Cartil Open. 2023;5(3):100385.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eO'Sullivan O, Ladlow P, Steiner K, Hillman C, Stocks J, Bennett AN, et al. Current status of catabolic, anabolic and inflammatory biomarkers associated with structural and symptomatic changes in the chronic phase of post-traumatic knee osteoarthritis\\u0026ndash; a systematic review. 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Molecular taxonomy of osteoarthritis for patient stratification, disease management and drug development: biochemical markers associated with emerging clinical phenotypes and molecular endotypes. Curr Opin Rheumatol. 2019;31(1):80\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRoemer FW, Crema MD, Trattnig S, Guermazi A. Advances in imaging of osteoarthritis and cartilage. Radiology. 2011;260(2):332\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEnglund M. The role of biomechanics in the initiation and progression of OA of the knee. Best Pract Res Clin Rheumatol. 2010;24(1):39\\u0026ndash;46.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKellgren JH, Lawrence J. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16(4):494.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRoos EM, Roos HP, Lohmander LS, Ekdahl C, Beynnon BD. 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Skeletal Radiol. 2023;52(11):2323\\u0026ndash;39.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWilfong JM, Badley EM, Perruccio AV. Old Before Their Time? The Impact of Osteoarthritis on Younger Adults. ;n/a(n/a): Arthritis Care \\u0026amp; Research; 2024.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eO\\u0026rsquo;Sullivan O, Behan F, Coppack R, Stocks J, Kluzek S, Valdes AM et al. Osteoarthritis in the United Kingdom Armed Forces: A Review of Its Impact, Treatment, and Future Research. BMJ Military Health. 2024(170):359\\u0026ndash;64.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePalmer D, Cooper D, Whittaker JL, Emery C, Batt ME, Engebretsen L, et al. Prevalence of and factors associated with osteoarthritis and pain in retired Olympians compared with the general population: part 1 \\u0026ndash; the lower limb. Br J Sports Med. 2022;56(19):1123.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFernandes GS, Parekh SM, Moses J, Fuller C, Scammell B, Batt ME, et al. Prevalence of knee pain, radiographic osteoarthritis and arthroplasty in retired professional footballers compared with men in the general population: a cross-sectional study. Br J Sports Med. 2018;52(10):678.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBennett AN, Dyball DM, Boos CJ, Fear NT, Schofield S, Bull AM, et al. Study protocol for a prospective, longitudinal cohort study investigating the medical and psychosocial outcomes of UK combat casualties from the Afghanistan war: the advance study. BMJ open. 2020;10(10):e037850.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBehan FP, Bennett AN, Watson F, Schofield S, O\\u0026rsquo;Sullivan O, Boos CJ, et al. Osteoarthritis after major combat trauma. The Armed Services Trauma Rehabilitation Outcome Study Rheumatology Advances in Practice; 2025.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eO'Sullivan O, Stocks J, Schofield S, Bilzon J, Boos CJ, Bull AMJ, et al. Association of serum biomarkers with radiographic knee osteoarthritis, knee pain and function in a young, male, trauma-exposed population - findings from the ADVANCE study. Osteoarthritis Cartilage. 2024;32(12):1636\\u0026ndash;46.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHollis B, Chatzigeorgiou C, Southam L, Hatzikotoulas K, Kluzek S, Williams A, et al. Lifetime risk and genetic predisposition to post-traumatic OA of the knee in the UK Biobank. Osteoarthritis Cartilage. 2023;31(10):1377\\u0026ndash;87.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eO\\u0026rsquo;Sullivan O, Watson F, Bull AMJ, Schofield S, Coady EC, Boos CJ et al. ,\\u003cem\\u003e. Influence of Major Trauma and Lower Limb Loss on Radiographic Progression and Incidence of Knee Osteoarthritis and Pain: A Comparative and Predictive Analysis from the ADVANCE Study. Arthritis Research and Therapy. 2025;under review.\\u003c/em\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAltman RD, Gold GE. Atlas of individual radiographic features in osteoarthritis, revised. Osteoarthr Cartil. 2007;15:A1\\u0026ndash;56.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHarris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42(2):377\\u0026ndash;81.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNehrer S, Ljuhar R, Steindl P, Simon R, Maurer D, Ljuhar D, et al. Automated Knee Osteoarthritis Assessment Increases Physicians' Agreement Rate and Accuracy: Data from the Osteoarthritis Initiative. Cartilage. 2021;13(1suppl):s957\\u0026ndash;65.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSmolle MA, Goetz C, Maurer D, Vielgut I, Novak M, Zier G, et al. Artificial intelligence-based computer-aided system for knee osteoarthritis assessment increases experienced orthopaedic surgeons' agreement rate and accuracy. Knee Surg Sports Traumatol Arthrosc. 2023;31(3):1053\\u0026ndash;62.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYoong S, Miles D, McKinney P, Smith I, Spencer N. A method of assigning socio-economic status classification to British armed forces personnel. J R Army Med Corps. 1999;145(3):140\\u0026ndash;2.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOffice for National Statistics. SOC 2020 Volume 3: the National Statistics Socio-economic Classification (NS-SEC rebased on the SOC 2020) 2021 [Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ons.gov.uk/methodology/classificationsandstandards/standardoccupationalclassificationsoc/soc2020/ soc2020volume3thenationalstatisticssocioeconomicclassificationnssecrebasedonthesoc2020\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ons.gov.uk/methodology/classificationsandstandards/standardoccupationalclassificationsoc/soc2020/ soc2020volume3thenationalstatisticssocioeconomicclassificationnssecrebasedonthesoc2020\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBertoli S, Leone A, Krakauer NY, Bedogni G, Vanzulli A, Redaelli VI, et al. Association of Body Shape Index (ABSI) with cardio-metabolic risk factors: A cross-sectional study of 6081 Caucasian adults. PLoS ONE. 2017;12(9):e0185013.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDing Z, Jarvis HL, Bennett AN, Baker R, Bull AMJ. Higher knee contact forces might underlie increased osteoarthritis rates in high functioning amputees: A pilot study. J Orthop Res. 2021;39(4):850\\u0026ndash;60.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBenjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc: Ser B (Methodol). 1995;57(1):289\\u0026ndash;300.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEMA. Guideline on clinical investigation of medicinal products used in the treatment of osteoarthritis. Committee for Medical Products for Human Use, European Medicines Agency; 2010.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFDA, Osteoarthritis. Structural Endpoints for the Development of Drugs, Devices, and Biological Products for Treatment Guidance for Industry. Federal Drugs Agency; 2018.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJordan JM. Cartilage oligomeric matrix protein as a marker of osteoarthritis. JOURNAL OF RHEUMATOLOGY-SUPPLEMENT-; 2004. pp. 45\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDaghestani HN, Kraus VB. Inflammatory biomarkers in osteoarthritis. Osteoarthritis Cartilage. 2015;23(11):1890\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFelson DT, Gale DR, Elon Gale M, Niu J, Hunter DJ, Goggins J, et al. Osteophytes and progression of knee osteoarthritis. Rheumatology. 2004;44(1):100\\u0026ndash;4.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSharma L, Song J, Felson DT, Cahue S, Shamiyeh E, Dunlop DD. The role of knee alignment in disease progression and functional decline in knee osteoarthritis. JAMA. 2001;286(2):188\\u0026ndash;95.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCasta\\u0026ntilde;eda S, Vicente EF. Osteoarthritis: More than Cartilage Degeneration. Clin Rev Bone Miner Metab. 2017;15(2):69\\u0026ndash;81.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLajeunesse D, Massicotte F, Pelletier JP, Martel-Pelletier J. Subchondral bone sclerosis in osteoarthritis: not just an innocent bystander. Mod Rheumatol. 2003;13(1):0007\\u0026ndash;14.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKraus VB, Collins JE, Charles HC, Pieper CF, Whitley L, Losina E, et al. Predictive validity of radiographic trabecular bone texture in knee osteoarthritis: the Osteoarthritis Research Society International/Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheumatol. 2018;70(1):80\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNiemczyk A, Waśkiel-Burnat A, Zaremba M, Czuwara J, Rudnicka L. The profile of adipokines associated with fibrosis and impaired microcirculation in systemic sclerosis. Adv Med Sci. 2023;68(2):298\\u0026ndash;305.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eScotece M, Conde J, Lopez V, Lago F, Pino J, G\\u0026oacute;mez-Reino JJ, et al. Adiponectin and leptin: new targets in inflammation. Basic Clin Pharmacol Toxicol. 2014;114(1):97\\u0026ndash;102.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKraus VB, Reed A, Soderblom EJ, Moseley MA, Hsueh M-F, Attur MG, et al. Serum proteomic panel validated for prediction of knee osteoarthritis progression. Osteoarthr Cartil Open. 2024;6(1):100425.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"arthritis-research-and-therapy\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"arrt\",\"sideBox\":\"Learn more about [Arthritis Research \\u0026 Therapy](http://arthritis-research.biomedcentral.com/)\",\"snPcode\":\"13075\",\"submissionUrl\":\"https://submission.nature.com/new-submission/13075/3\",\"title\":\"Arthritis Research \\u0026 Therapy\",\"twitterHandle\":\"@ArthritisRes\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"pathophysiology, serum biomarkers, anabolism, catabolism, inflammation, post-traumatic osteoarthritis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6120483/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6120483/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eIntroduction\\u003c/p\\u003e \\u003cp\\u003ePost-traumatic osteoarthritis (PTOA) is a complex condition with multiple pathological processes at play. Molecular biomarkers can enable a better understanding of these processes, thus enhancing case endotyping, phenotyping, personalised care and drug discovery. The longitudinal ArmeD SerVices TrAuma and RehabilitatioN OutComE (ADVANCE) study offers the opportunity to develop insights into PTOA pathophysiology using a panel of extra-cellular matrix turnover, inflammatory and metabolic biomarkers and their cross-sectional (associative) and longitudinal (predictive) relationship to features of radiographic PTOA in a cohort of young, male, severely injured servicepersonnel.\\u003c/p\\u003e \\u003cp\\u003eMethods\\u003c/p\\u003e \\u003cp\\u003eUsing serum and radiographic data gathered in the baseline (8-years) and first follow-up visit (11-years) post-injury of ADVANCE (n\\u0026thinsp;=\\u0026thinsp;1145), two analyses were undertaken. Firstly, cross-sectional univariate analysis between serum COMP, CTX-II, PIIANP, IL-1b, IL-17a, TNF-a, leptin and adiponectin and radiographic features (joint space narrowing (JSN), osteophytes and sclerosis), followed by the longitudinal prediction of new or progression of these three radiographic features using LASSO to select predictors. The area under a ROC curve (AUROC) was computed.\\u003c/p\\u003e \\u003cp\\u003eResults\\u003c/p\\u003e \\u003cp\\u003eComplete radiographic and serum case data in n\\u0026thinsp;=\\u0026thinsp;872 male British servicemen, aged 34.5 (5.5) at baseline and 38.3 (5.4) at follow-up were analysed. Those with JSN had significantly higher concentrations of leptin (FDR-corrected q-value, q\\u0026thinsp;=\\u0026thinsp;0.04). COMP had an AUROC of 0.604 (0.543,0.664) for new cases of JSN, COMP, IL-1β and leptin had an AUROC 0.586 (0.524,0.646) for new osteophytes, and TNF-α, IL-1β and adiponectin had an AUROC 0.590 (0.520,0.659) for new sclerosis.\\u003c/p\\u003e \\u003cp\\u003eConclusion\\u003c/p\\u003e \\u003cp\\u003eThis large, unique study suggests different pathological processes underpinning each radiographic feature of PTOA, including predominant unbalanced ECM-catabolism and inflammation contributing to JSN, ECM-catabolism and increased inflammation contributing to osteophyte development and an inflammation-predominant process contributing to subchondral sclerosis.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Insights into knee post-traumatic osteoarthritis pathophysiology from the relationship of serum biomarkers to radiographic features in the ADVANCE cohort\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-04-21 08:18:08\",\"doi\":\"10.21203/rs.3.rs-6120483/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-06-16T06:15:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-06-15T20:27:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"298874615182311261302401130851057275034\",\"date\":\"2025-05-01T14:48:43+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-04-02T01:07:58+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"291059681783102271743502194331611666694\",\"date\":\"2025-03-10T13:38:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-03-10T08:07:48+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-03-05T07:48:49+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-03-05T06:53:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Arthritis Research \\u0026 Therapy\",\"date\":\"2025-02-27T11:14:09+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"arthritis-research-and-therapy\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"arrt\",\"sideBox\":\"Learn more about [Arthritis Research \\u0026 Therapy](http://arthritis-research.biomedcentral.com/)\",\"snPcode\":\"13075\",\"submissionUrl\":\"https://submission.nature.com/new-submission/13075/3\",\"title\":\"Arthritis Research \\u0026 Therapy\",\"twitterHandle\":\"@ArthritisRes\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"b6a17d95-5456-4d34-9f63-f28f9647d20e\",\"owner\":[],\"postedDate\":\"April 21st, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-10T16:04:35+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6120483\",\"link\":\"https://doi.org/10.1186/s13075-025-03648-y\",\"journal\":{\"identity\":\"arthritis-research-and-therapy\",\"isVorOnly\":false,\"title\":\"Arthritis Research \\u0026 Therapy\"},\"publishedOn\":\"2025-11-05 15:56:51\",\"publishedOnDateReadable\":\"November 5th, 2025\"},\"versionCreatedAt\":\"2025-04-21 08:18:08\",\"video\":\"\",\"vorDoi\":\"10.1186/s13075-025-03648-y\",\"vorDoiUrl\":\"https://doi.org/10.1186/s13075-025-03648-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6120483\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6120483\",\"identity\":\"rs-6120483\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}