Comprehensive analysis on subchondral bone marrow lesions of human osteoarthritis by integrating bulk and single-cell transcriptomes.

OBJECTIVE: This study aims to demonstrate the cellular composition and underlying mechanisms in subchondral bone marrow lesions (BMLs) of knee osteoarthritis (OA). METHODS: BMLs were assessed by MRI Osteoarthritis Knee Score (MOAKS)≥2. Bulk RNA-sequencing (bulk-seq) and BML-specific differentially expressed genes (DEGs) analysis were performed among subchondral bone samples (including OA-BML=3, paired OA-NBML=3; non-OA=3). The hub genes of BMLs were identified by verifying in independent datasets and multiple bioinformatic analyses. To further estimate cell-type composition of subchondral bone, we utilized two newly developed deconvolution algorithms (MuSiC, MCP-counter) in transcriptomic datasets, based on signatures from open-accessed single-cell RNA sequencing (scRNA-seq). Finally, competing endogenous RNA (ceRNA) and transcription factor (TF) networks were constructed through multiple predictive databases, and validated by public non-coding RNA profiles. RESULTS: A total of 86 BML-specific DEGs (up 79, down 7) were identified. IL11 and VCAN were identified as core hub genes. The "has-miR-424-5p/lncRNA PVT1" was determined as crucial network, targeting IL11 and VCAN, respectively. More importantly, two deconvolution algorithms produced approximate estimations of cell-type composition, and the cluster of heterotopic-chondrocyte was discovered abundant in BMLs, and positively correlated with the expression of hub genes. CONCLUSION: IL11 and VCAN were identified as the core hub genes of BMLs, and their molecular networks were determined as well. We profiled the characteristics of subchondral bone at single-cell level and determined that the heterotopic-chondrocyte was abundant in BMLs and was closely linked to IL11 and VCAN. Our study may provide new insights into the microenvironment and pathological molecular mechanism of BMLs, and could lead to novel therapeutic strategies.

Signal intensity alteration and maximal area of pericruciate fat pad are associated with incident radiographic osteoarthritis: data from the Osteoarthritis Initiative.

OBJECTIVE: To determine whether pericruciate fat pad (PCFP) signal intensity alteration and maximal area are associated with incident radiographic osteoarthritis (ROA) over 4 years in the Osteoarthritis Initiative (OAI) study. METHODS: Participants were from the Osteoarthritis Initiative (OAI) study. Case knees (n = 355) were defined by incident ROA between 12 and 48 months visits and were matched by sex, age, and radiographic status with control knees (n = 355). Magnetic resonance images (MRIs) were used to assess PCFP signal intensity alteration and PCFP maximal area at P0 (time of onset of ROA), P-1 (1 year prior to P0), and baseline. Conditional logistic regression analyses were applied to assess associations between PCFP measures and the risk of incident ROA. RESULTS: The mean age of participants was 60.1 years and 66.9% were women. In multivariable analyses, PCFP signal intensity alteration measured at three time points (OR [95%CI]: 1.28 [1.10-1.50], 1.52 [1.30-1.78], 1.50 [1.27-1.76], respectively) and PCFP maximal area (OR [95%CI]: 1.21 [1.03-1.42], 1.27 [1.07-1.52], 1.37 [1.15-1.62], respectively) were significantly associated with incident ROA. CONCLUSIONS: PCFP signal intensity alteration and maximal area were associated with incident ROA over 4 years, implying that they may have roles to play in ROA. KEY POINTS: • Pericruciate fat pad signal intensity alteration and maximal area were associated with incident ROA, implying that they may have roles to play in ROA.

Palladium-catalyzed carbonylation of benzylic ammonium salts to amides and esters via C-N bond activation.

An efficient palladium-catalyzed carbonylation reaction of readily available quaternary ammonium salts with CO is reported for the first time to afford arylacetamides and arylacetic acid esters via benzylic C-N bond cleavage. This protocol features mild reaction conditions under atmospheric pressure of CO, a redox-neutral process without an additional oxidant, and a broad substrate scope for various kinds of amines, alcohols and phenols.

Associations of childhood-to-adulthood body size trajectories and genetic susceptibility with the risks of osteoarthritis: a population-based cohort study of UK Biobank data.

BACKGROUND: Large adulthood body size was associated with increased risk of osteoarthritis. We aimed to examine the association between body size trajectories from childhood to adulthood and potential interactions with genetic susceptibility on osteoarthritis risk. METHODS: We included participants from the UK Biobank aged 38-73 years in 2006-10. Childhood body size information was collected by questionnaire. Adulthood BMI was assessed and transformed into three categories (<25 kg/m2 for normal, 25-29·9 kg/m2 for overweight, and >30 kg/m2 for obesity). A Cox proportional hazards regression model was applied to assess the association between body size trajectories and osteoarthritis incidence. Osteoarthritis-related polygenic risk score (PRS) was constructed to evaluate its interactions with body size trajectories on osteoarthritis risk. FINDINGS: For the 466 292 participants included, we identified nine body size trajectories [thinner to normal (11·6%), overweight (17·2%), or obesity (26·9%); average to normal (11·8%), overweight (16·2%), or obesity (23·7%); and plumper to normal (12·3%), overweight (16·2%), or obesity (23·6%)]. Compared with individuals in the average-to-normal group, all other trajectory groups had higher risks of osteoarthritis, after adjustment for demographic, social-economic and lifestyle covariates (hazard ratios [HRs] 1·05-2·41; all p<0·01). Among them, thinner-to-obesity (HR 2·41; 95% CI 2·23-2·49) had the most prominent association with increased osteoarthritis risk. A high PRS was significantly associated with an increased risk of osteoarthritis (1·14; 1·11-1·16), whereas no interaction between childhood-to-adulthood body size trajectories and PRS on osteoarthritis risks was observed. The population attributable fraction suggested that body size towards normal in adulthood could eliminate osteoarthritis cases by 18·67% for thinner-to-overweight to 38·74% for plumper-to-obesity. INTERPRETATION: Average-to-normal body size seems to be the healthiest childhood-to-adulthood trajectory for osteoarthritis risk, whereas a trajectory of increased body size from thinner to obesity has the highest risk for osteoarthritis. These associations are independent of osteoarthritis genetic susceptibility. FUNDING: The National Natural Science Foundation of China (32000925) and Guangzhou Science and Technology Program (202002030481).

The application of machine learning in early diagnosis of osteoarthritis: a narrative review.

Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

A model-based quantitative analysis of efficacy and associated factors of platelet rich plasma treatment for osteoarthritis.

OBJECTIVE: While platelet rich plasma (PRP) has been extensively studied in treating osteoarthritis (OA), there has been an ongoing debate regarding the efficacy of PRP and the optimal subpopulation for PRP treatment remains unknown. The authors hereby aim to establish a pharmacodynamic model-based meta-analysis to quantitatively evaluate PRP efficacy, comparing with hyaluronic acid (HA) and identify relevant factors that significantly affect the efficacy of PRP treatment for OA. METHODS: The authors searched for PubMed and the Cochrane Library Central Register of Controlled Trials of PRP randomized controlled trials (RCTs) for the treatment of symptomatic or radiographic OA from the inception dates to 15 July 2022. Participants' clinical and demographic characteristics and efficacy data, defined as Western Ontario and McMaster Universities Osteoarthritis Index and visual analog scale pain scores at each time point were extracted. RESULTS: A total of 45 RCTs (3829 participants) involving 1805 participants injected with PRP were included in the analysis. PRP reached a peak efficacy at ~ 2-3 months after injection in patients with OA. Both conventional meta-analysis and pharmacodynamic maximal effect models showed that PRP was significantly more effective than HA for joint pain and function impairment (additional decrease of 1.1, 0.5, 4.3, and 1.1 scores compared to HA treatment at 12 months for Western Ontario and McMaster Universities Osteoarthritis Index pain, stiffness, function, and visual analog scale pain scores, respectively). Higher baseline symptom scores, older age (≥60 years), higher BMI (≥30), lower Kellgren-Lawrence grade (≤2) and shorter OA duration (<6 months) were significantly associated with greater efficacy of PRP treatment. CONCLUSION: These findings sugges t that PRP is a more effective treatment for OA than the more well-known HA treatment. The authors also determined the time when the PRP injection reaches peak efficacy and optimized the targeting subpopulation of OA. Further high-quality RCTs are required to confirm the optimal population of PRP in the treatment of OA.

Osteoporosis and osteoarthritis: a bi-directional Mendelian randomization study.

OBJECTIVE: To investigate the causal relationship between low bone mineral density (BMD) and osteoarthritis (OA) using Mendelian randomization (MR) design. METHODS: Two-sample bi-directional MR analyses were performed using summary-level information on OA traits from UK Biobank and arcOGEN. Sensitivity analyses including MR-Egger, simple median, weighted median, MR pleiotropy residual sum, and outlier approaches were utilized in conjunction with inverse variance weighting (IVW). Gene ontology (GO) enrichment analyses and expression quantitative trait locus (eQTL) colocalization analyses were used to investigate the potential mechanism and shared genes between osteoporosis (OP) and OA. RESULTS: The IVW method revealed that genetically predicted low femoral neck BMD was significantly linked with hip (ß = 0.105, 95% CI: 0.023-0.188) and knee OA (ß = 0.117, 95% CI: 0.049-0.184), but not with other site-specific OA. Genetically predicted low lumber spine BMD was significantly associated with OA at any sites (ß = 0.048, 95% CI: 0.011-0.085), knee OA (ß = 0.101, 95% CI: 0.045-0.156), and hip OA (ß = 0.150, 95% CI: 0.077-0.224). Only hip OA was significantly linked with genetically predicted reduced total bone BMD (ß = 0.092, 95% CI: 0.010-0.174). In the reverse MR analyses, no evidence for a causal effect of OA on BMD was found. GO enrichment analysis and eQTL analysis illustrated that DDN and SMAD-3 were the most prominent co-located genes. CONCLUSIONS: These findings suggested that OP may be causally linked to an increased risk of OA, indicating that measures to raise BMD may be effective in preventing OA. More research is required to determine the underlying processes via which OP causes OA.

Relative Efficacy and Safety of Anti-Inflammatory Biologic Agents for Osteoarthritis: A Conventional and Network Meta-Analysis.

Previous studies have consistently revealed that both local and systemic inflammations are the key to the onset and progression of osteoarthritis (OA). Thus, anti-inflammatory biologic agents could potentially attenuate the progression of OA. We conducted this meta-analysis to examine the efficacy and safety of ant-inflammatory biologic agents among OA patients. METHODS: Five databases were searched for randomized controlled trials (RCTs) comparing biologics with placebo or each other in OA patients. Data of pain, physical function, stiffness, and adverse events (AEs) were extracted for a conventional and a Bayesian network meta-analysis. RESULTS: 15 studies with data for 1566 patients were analyzed. In the conventional meta-analysis, etanercept (SMD -0.47; 95% CI -0.89, -0.05) and infliximab (SMD -2.04; CI -2.56, -1.52) were superior to placebo for knee pain. In the network meta-analysis, infliximab was superior to all the other biologic agents in improving pain (vs. hyaluronic acid (SMD -22.95; CI -34.21, -10.43), vs. adalimumab (SMD -21.71; CI -32.65, -11.00), vs. anakinra (SMD -24.63; CI -38.79, -10.05), vs. canakinumab (SMD -32.83; CI -44.45, -20.68), vs. etanercept (SMD -18.40; CI -29.93, -5.73), vs. lutikizumab (SMD -25.11; CI -36.47, -14.78), vs. naproxen (SMD -30.16; CI -41.78, -17.38), vs. tocilizumab (SMD -24.02; CI -35.63, -11.86) and vs. placebo (SMD -25.88; CI -34.87, -16.60)). No significant differences were observed between biologics and placebo regarding physical function, stiffness, and risk of AEs. CONCLUSIONS: The findings suggest that infliximab may relieve pain more than other biological agents in OA patients. No significant differences were observed between biologics and placebo regarding physical function, stiffness, and risk of AEs. The results must be interpreted cautiously; therefore, further randomized controlled trials are warranted.

Association between Metformin Use and Risk of Total Knee Arthroplasty and Degree of Knee Pain in Knee Osteoarthritis Patients with Diabetes and/or Obesity: A Retrospective Study.

Objectives: We aimed to examine whether metformin (MET) use is associated with a reduced risk of total knee arthroplasty (TKA) and low severity of knee pain in patients with knee osteoarthritis (OA) and diabetes and/or obesity. Methods: Participants diagnosed with knee OA and diabetes and/or obesity from June 2000 to July 2019 were selected from the information system of a local hospital. Regular MET users were defined as those with recorded prescriptions of MET or self-reported regular MET use for at least 6 months. TKA information was extracted from patients' surgical records. Knee pain was assessed using the numeric rating scale. Log-binomial regression, linear regression, and propensity score weighting (PSW) were performed for statistical analyses. Results: A total of 862 participants were included in the analyses. After excluding missing data, there were 346 MET non-users and 362 MET users. MET use was significantly associated with a reduced risk of TKA (prevalence ratio: 0.26, 95% CI: 0.15 to 0.45, p < 0.001), after adjustment for age, gender, body mass index, various analgesics, and insurance status. MET use was significantly associated with a reduced degree of knee pain after being adjusted for the above covariates (ß: −0.48, 95% CI: −0.91 to −0.05, p = 0.029). There was a significantly accumulative effect of MET use on the reduced risk of TKA. Conclusion: MET can be a potential therapeutic option for OA. Further clinical trials are needed to determine if MET can reduce the risk of TKA and the severity of knee pain in metabolic-associated OA patients.

Osteophytes mediate the associations between cartilage morphology and changes in knee symptoms in patients with knee osteoarthritis.

AIMS: To investigate whether the associations between cartilage defects and cartilage volumes with changes in knee symptoms were mediated by osteophytes. METHODS: Data from the Vitamin D Effects on Osteoarthritis (VIDEO) study were analyzed as a cohort. The Western Ontario and McMaster Universities Osteoarthritis Index was used to assess knee symptoms at baseline and follow-up. Osteophytes, cartilage defects, and cartilage volumes were measured using magnetic resonance imaging at baseline. Associations between cartilage morphology and changes in knee symptoms were assessed using linear regression models, and mediation analysis was used to test whether these associations were mediated by osteophytes. RESULTS: A total of 334 participants (aged 50 to 79 years) with symptomatic knee osteoarthritis were included in the analysis. Cartilage defects were significantly associated with change in total knee pain, change in weight-bearing pain, and change in non-weight-bearing pain after adjustment for age, sex, body mass index, and intervention. Cartilage volume was significantly associated with change in weight-bearing pain and change in physical dysfunction after adjustment. Lateral tibiofemoral and patellar osteophyte mediated the associations of cartilage defects with change in total knee pain (49-55%) and change in weight-bearing pain (61-62%) and the association of cartilage volume with change in weight-bearing pain (27-30%) and dysfunction (24-25%). Both cartilage defects and cartilage volume had no direct effects on change in knee symptoms. CONCLUSIONS: The significant associations between cartilage morphology and changes in knee symptoms were indirect and were partly mediated by osteophytes.

Speed, speed variation and crash relationships for urban arterials.

Speed and speed variation are closely associated with traffic safety. There is, however, a dearth of research on this subject for the case of urban arterials in general, and in the context of developing nations. In downtown Shanghai, the traffic conditions in each direction are very different by time of day, and speed characteristics during peak hours are also greatly different from those during off-peak hours. Considering that traffic demand changes with time and in different directions, arterials in this study were divided into one-way segments by the direction of flow, and time of day was differentiated and controlled for. In terms of data collection, traditional fixed-based methods have been widely used in previous studies, but they fail to capture the spatio-temporal distributions of speed along a road. A new approach is introduced to estimate speed variation by integrating spatio-temporal speed fluctuation of a single vehicle with speed differences between vehicles using taxi-based high frequency GPS data. With this approach, this paper aims to comprehensively establish a relationship between mean speed, speed variation and traffic crashes for the purpose of formulating effective speed management measures, specifically using an urban dataset. From a total of 234 one-way road segments from eight arterials in Shanghai, mean speed, speed variation, geometric design features, traffic volume, and crash data were collected. Because the safety effects of mean speed and speed variation may vary at different segment lengths, arterials with similar signal spacing density were grouped together. To account for potential correlations among these segments, a hierarchical Poisson log-normal model with random effects was developed. Results show that a 1% increase in mean speed on urban arterials was associated with a 0.7% increase in total crashes, and larger speed variation was also associated with increased crash frequency.

Safety modeling of urban arterials in Shanghai, China.

Traffic safety on urban arterials is influenced by several key variables including geometric design features, land use, traffic volume, and travel speeds. This paper is an exploratory study of the relationship of these variables to safety. It uses a comparatively new method of measuring speeds by extracting GPS data from taxis operating on Shanghai's urban network. This GPS derived speed data, hereafter called Floating Car Data (FCD) was used to calculate average speeds during peak and off-peak hours, and was acquired from samples of 15,000+ taxis traveling on 176 segments over 18 major arterials in central Shanghai. Geometric design features of these arterials and surrounding land use characteristics were obtained by field investigation, and crash data was obtained from police reports. Bayesian inference using four different models, Poisson-lognormal (PLN), PLN with Maximum Likelihood priors (PLN-ML), hierarchical PLN (HPLN), and HPLN with Maximum Likelihood priors (HPLN-ML), was used to estimate crash frequencies. Results showed the HPLN-ML models had the best goodness-of-fit and efficiency, and models with ML priors yielded estimates with the lowest standard errors. Crash frequencies increased with increases in traffic volume. Higher average speeds were associated with higher crash frequencies during peak periods, but not during off-peak periods. Several geometric design features including average segment length of arterial, number of lanes, presence of non-motorized lanes, number of access points, and commercial land use, were positively related to crash frequencies.