Risk of carpal tunnel syndrome among patients with osteoarthritis: a US population-based study.

BACKGROUND: Carpal tunnel syndrome (CTS), an entrapment neuropathy caused by pressure of the median nerve, is a progressive condition that can lead to a decreased quality of life. Studies suggest an association between CTS and arthritis; however, previous studies examining osteoarthritis (OA) and CTS are limited in number, scope and study design. This study estimated the incidence and risk of CTS among patients with OA, both overall and by specific joints, in a large population-based cohort in the United States. METHODS: Patients from the Optum claims database aged ≥ 45 years and diagnosed with OA between January 1, 2018, and December 31, 2022, were eligible for the OA cohort. The non-OA cohort included those without a diagnosis of OA at the index date and no history of OA for 12 months pre-index. Baseline characteristics were balanced using propensity score matching. The risk of CTS in the OA and non-OA cohort were evaluated using incidence rates and adjusted hazard ratios that were estimated using Cox regression. RESULTS: After applying the inclusion/exclusion criteria, 3,610,240 of the 6,023,384 adults with a diagnosis of OA remained in the OA cohort. After propensity-score matching, each cohort included 1,033,439 individuals. The incidence rates for CTS per 1000 person-years were 7.35 (95% confidence interval [CI] 7.21-7.49) in the OA cohort and 1.44 (95% CI 1.38-1.50) in the non-OA cohort. The risk of developing CTS in patients with OA was ~ 4 times that of patients without (hazard ratio = 3.80; 95% CI 3.54-4.07). This increased risk was found across all OA joint types, with OA of the hand/wrist having the highest risk for CTS. Additionally, multiple OA joints presented a higher risk compared with a single affected joint. CONCLUSIONS: OA increases the risk of CTS, but this is not limited to patients with hand/wrist OA, suggesting a systemic impact of OA on CTS. While the risk appears highest for patients with hand/wrist OA, patients with more distant affected joints like knee or hip also have an increased risk of CTS.

Development and validation of a frailty index for use in the osteoarthritis initiative.

BACKGROUND: The Osteoarthritis Initiative (OAI) evaluates the development and progression of osteoarthritis. Frailty captures the heterogeneity in aging. Use of this resource-intensive dataset to answer aging-related research questions could be enhanced by a frailty measure. OBJECTIVE: To: (i) develop a deficit accumulation frailty index (FI) for the OAI; (ii) examine its relationship with age and compare between sexes, (iii) validate the FI versus all-cause mortality and (iv) compare this association with mortality with a modified frailty phenotype. DESIGN: OAI cohort study. SETTING: North America. SUBJECTS: An FI was determined for 4,755/4,796 and 4,149/4,796 who had a valid FI and frailty phenotype. METHODS: Fifty-nine-variables were screened for inclusion. Multivariate Cox regression evaluated the impact of FI or phenotype on all-cause mortality at follow-up (up to 146 months), controlling for age and sex. RESULTS: Thirty-one items were included. FI scores (0.16 ± 0.09) were higher in older adults and among females (both, P < 0.001). By follow-up, 264 people had died (6.4%). Older age, being male, and greater FI were associated with a higher risk of all-cause mortality (all, P < 0.001). The model including FI was a better fit than the model including the phenotype (AIC: 4,167 vs. 4,178) and was a better predictor of all-cause mortality than the phenotype with an area under receiver operating characteristic curve: 0.652 vs. 0.581. CONCLUSION: We developed an FI using the OAI and validated it in relation to all-cause mortality. The FI may be used to study aging on clinical, functional and structural aspects of osteoarthritis included in the OAI.

Imaging of Rheumatic Diseases Affecting the Lower Limb.

Imaging methods capable of detecting inflammation, such as MR imaging and ultrasound, are of paramount importance in rheumatic disease management, not only for diagnostic purposes but also for monitoring disease activity and treatment response. However, more advanced stages of arthritis, characterized by findings of cumulative structural damage, have traditionally been accomplished by radiographs and computed tomography. The purpose of this review is to provide an overview of imaging of some of the most prevalent inflammatory rheumatic diseases affecting the lower limb (osteoarthritis, rheumatoid arthritis, and gout) and up-to-date recommendations regarding imaging diagnostic workup.

Risk for cardiovascular disease development in rheumatoid arthritis.

BACKGROUND: Patients with rheumatoid arthritis have significant cardiovascular mortality and morbidity. OBJECTIVE: To investigate the effects of chronic inflammation in rheumatoid arthritis on cardiovascular morbidity association with cardiovascular risk factors risk factors. Mortality report is secondary just to show trends without sufficient statistical power as it is accidental endpoint. METHODS: A total of 201 individuals without previous cardiovascular disease, 124 with rheumatoid arthritis (investigation group) and 77 with osteoarthritis (control group), were included in the study and followed up for an average of 8 years to assess the development of fatal or non-fatal cardiovascular diseases. The incidence and prevalence of cardiovascular risk factors were also investigated. RESULTS: The total incidence of one or more fatal or nonfatal cardiovascular events was 43.9% in the investigation group and 37.5% in the control group. Of these patients, 31.7% and 30.9% survived cardiovascular events in the investigation and control groups, respectively. The most common cardiovascular disease among participants who completed the study and those who died during the study was chronic heart failure. The results of the subgroup analysis showed that strict inflammation control plays a central role in lowering cardiovascular risk. CONCLUSION: A multidisciplinary approach to these patients is of paramount importance, especially with the cooperation of immunologists and cardiologists for early detection, prevention, and management of cardiovascular risks and diseases.

Comprehensive Analysis of Sphingolipid Metabolism-Related Genes in Osteoarthritic Diagnosis and Synovial Immune Dysregulation.

BACKGROUND Osteoarthritis (OA) is a chronic degenerative disease characterized by synovitis and has been implicated in sphingolipid metabolism disorder. However, the role of sphingolipid metabolism pathway (SMP)-related genes in the occurrence of OA and synovial immune dysregulation remains unclear. MATERIAL AND METHODS In this study, we obtained synovium-related databases from GEO (n=40 for both healthy controls and OA) and analyzed the expression levels of SMP-related genes. Using 2 algorithms, we identified hub genes and developed a diagnostic model incorporating these hub genes to predict the occurrence of OA. Subsequently, the hub genes were further validated in peripheral blood samples from OA patients. Additionally, CIBERSORT and MCP-counter analyses were employed to explore the correlation between hub genes and immune dysregulation in OA synovium. WGCNA was used to determine enriched modules in different clusters. RESULTS Overall, the expression levels of SMP genes were upregulated in OA synovium. We identified 6 hub genes of SMP and constructed an excellent diagnostic model (AUC=0.976). The expression of re-confirmed hub genes showed associations with immune-related cell infiltration and levels of inflammatory cytokines. Furthermore, we observed heterogeneity in the expression patterns of hub genes across different clusters of OA. Notably, older patients displayed increased susceptibility to elevated levels of pain-related inflammatory cytokines and infiltration of immune cells. CONCLUSIONS The SMP-related hub genes have the potential to serve as diagnostic markers for OA patients. Moreover, the 4 hub genes of SMP demonstrate wide participation in immune dysregulation in OA synovium. The activation of different pathways is observed among different populations of patients with OA.

Evaluation of forelimb gait variation overground at a walk in sound and lame dogs using a combination of diagnostic techniques.

BACKGROUND: Kinetic and kinematic gait analysis is increasingly practised as a part of lameness evaluation in dogs. The aim of this study was to examine the normal short- and long-term variation in forelimb gait in sound control dogs (CD) at a walk using seven selected variables of objective kinetic and kinematic gait analyses. Also, to compare the findings in CD to a group of forelimb lame dogs with elbow osteoarthritis (OAD). An additional aim was to test a kinetic based graphic method for lameness detection; symmetry squares (SS). A prospective longitudinal study was carried out on client owned CD and OAD. Clinical and orthopaedic evaluations were performed to ensure soundness and detect and grade lameness. Seven kinetic and kinematic variables and SS were tested for lameness evaluation. The CD were divided into two subgroups, CD1 and CD2, and examined twice: CD1 with two months interval and CD2 with 3-4 h interval. The OAD group was evaluated once and compared to the CD groups' first examination. RESULTS: Thirteen CD and 19 OAD were included. For CD1 and CD2, there were no significant differences in any examined variable between examination occasions. Total peak force/impulse symmetry and fore-hind peak force/impulse symmetry differed significantly between OAD and CD. Symmetry squares had a 74% agreement to subjective orthopaedic evaluations. CONCLUSIONS: In CD, no difference in the examined variables was seen between examination occasions. Four out of seven objective variables differed significantly between CD and OAD. The graphic SS method might have diagnostic potential for lameness detection, making it possible to detect a shift from lame to non-lame limbs. Potentially, this might be especially helpful in bilaterally lame dogs, which often represent a clinical challenge in lameness evaluation.

[Osteoarthritis of the wrist]. / Arthrosen des Handgelenks.

The term osteoarthritis (OA) of the wrist can be used as an umbrella term for various, often independent areas of OA, as the wrist is made up of several joints. Radiocarpal OA often occurs after untreated ligament injuries, incorrectly healed bone fractures in the carpus or after radius fractures involving the joint. A typical sequence of propagation is known for radiocarpal OA following scapholunate (SL) insufficiency or scaphoid pseudarthrosis. Other causes include inflammation, crystal deposits or bone necrosis. Ulnocarpal arthrosis occurs posttraumatically or primarily when there are differences in levels between the ulna and radius. When treating wrist arthrosis, after conservative measures have been exhausted a surgical procedure should be chosen that enables the best possible load-bearing and residual mobility, considering the surgical risks and individual requirements. During salvage operations, the defective cartilage areas are either fused directly or eliminated using appropriate diverting partial fusions and resection arthroplasty. An accurate analysis of the affected zones is crucial for selecting an appropriate intervention.

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches.

BACKGROUND: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited. METHODS: In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We employed machine learning algorithms, least absolute shrinkage and selection operator(LASSO), and support vector machine recursive feature elimination(SVM-RFE) to select potential diagnostic markers. Then the gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential diagnostic markers' involvement in biological pathways. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with OA. RESULTS: We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. Seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) were identified as the best characteristic genes for distinguishing OA from healthy samples. We performed clustering analysis and identified two distinct subtypes of OA, C1, and C2, with differential gene expression and immune cell infiltration profiles. Then we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity. CONCLUSIONS: Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA.

As society ages, the incidence of knee osteoarthritis continues to rise, bringing with it a series of social impacts and medical pressure. Despite the increasing recognition of the role of ion channels in the pathogenesis of OA, diagnostic markers and targeted therapies remain limited.This study investigated the role of TRP as possible diagnostic tools for OA.Seven TRP-related genes were identified as the best traits to distinguish OA from healthy samples, and then we constructed and validated risk scores for three key genes (PPP1R3D, ZNF101, and LOC651309) relevant to OA ion channel gene modules.Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer a reference for further clinical diagnosis.

Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis.

Objective: Seronegative rheumatoid arthritis (RA) is defined as RA without circulating autoantibodies such as rheumatoid factor and anti-citrullinated protein antibodies; thus, early diagnosis of seronegative RA can be challenging. Here, we aimed to identify diagnostic biomarkers for seronegative RA by performing lipidomic analyses of sera and urine samples from patients with RA. Methods: We performed untargeted lipidomic analysis of sera and urine samples from 111 RA patients, 45 osteoarthritis (OA) patients, and 25 healthy controls (HC). These samples were divided into a discovery cohort (n = 97) and a validation cohort (n = 84). Serum samples from 20 patients with systemic lupus erythematosus (SLE) were also used for validation. Results: The serum lipidome profile of RA was distinguishable from that of OA and HC. We identified a panel of ten serum lipids and three urine lipids in the discovery cohort that showed the most significant differences. These were deemed potential lipid biomarker candidates for RA. The serum lipid panel was tested using a validation cohort; the results revealed an accuracy of 79%, a sensitivity of 71%, and a specificity of 86%. Both seropositive and seronegative RA patients were differentiated from patients with OA, SLE, and HC. Three urinary lipids showing differential expression between RA from HC were identified with an accuracy of 84%, but they failed to differentiate RA from OA. There were five lipid pathways that differed between seronegative and seropositive RA. Conclusion: Here, we identified a panel of ten serum lipids as potential biomarkers that can differentiate RA from OA and SLE, regardless of seropositivity. In addition, three urinary lipids had diagnostic utility for differentiating RA from HC.

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning.

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-ß signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

Mendelian randomization and transcriptome analysis identified immune-related biomarkers for osteoarthritis.

Background: The immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data. Methods: The discovery dataset integrated the GSE89408 and GSE143514 datasets to identify biomarkers that were significantly associated with the OA immune microenvironment through multiple machine learning methods and weighted gene co-expression network analysis (WGCNA). The identified signature genes were confirmed using two independent validation datasets. We also performed a two-sample mendelian randomization (MR) study to generate causal relationships between biomarkers and OA using OA genome-wide association study (GWAS) summary data (cases n = 24,955, controls n = 378,169). Inverse-variance weighting (IVW) method was used as the main method of causal estimates. Sensitivity analyses were performed to assess the robustness and reliability of the IVW results. Results: Three signature genes (FCER1G, HLA-DMB, and HHLA-DPA1) associated with the OA immune microenvironment were identified as having good diagnostic performances, which can be used as biomarkers. MR results showed increased levels of FCER1G (OR = 1.118, 95% CI 1.031-1.212, P = 0.041), HLA-DMB (OR = 1.057, 95% CI 1.045 -1.069, P = 1.11E-21) and HLA-DPA1 (OR = 1.030, 95% CI 1.005-1.056, P = 0.017) were causally and positively associated with the risk of developing OA. Conclusion: The present study identified the 3 potential immune-related biomarkers for OA, providing new perspectives for the prevention and treatment of OA. The MR study provides genetic support for the causal effects of the 3 biomarkers with OA and may provide new insights into the molecular mechanisms leading to the development of OA.

Global burden of osteoarthritis in adults aged 30 to 44 years, 1990 to 2019: results from the Global Burden of Disease Study 2019.

BACKGROUND: Osteoarthritis (OA) is a common orthopedic disorder, and its incidence has been increasing among young adults in recent years. The purpose of this study is to investigate the global, regional, and national trends in OA burden and variation among individuals aged 30 to 44 from 1990 to 2019. METHODS: Data on the incidence, prevalence, and years lived with disability (YLDs) related to OA were sourced from the Global Burden of Disease Study 2019 among individuals aged 30 to 44. These measures were stratified by gender, region, country, and socio-demographic index (SDI). Additionally, we analyzed YLDs attributable to risk factors. RESULTS: In 2019, there were a total of 32,971,701 cases of OA among individuals aged 30 to 44 years worldwide, with an additional 7,794,008 new incident cases reported. OA of the knee was the primary contributor to both incidence and prevalence rates over the past three decades. From 1990 to 2019, both males and females in countries with high SDI and high-middle SDI showed upward trends in age-standardized incidence, prevalence, and YLDs rates. In 2019, the United States of America had the highest age-standardized incidence, prevalence, and YLDs rates. Elevated body-mass index (BMI) was found to be the most prevalent risk factor for osteoarthritis-related YLDs. Age-standardized YLDs rates were positively associated with SDI. CONCLUSIONS: OA remains a significant disease burden on individuals aged 30 to 44, with modifiable risk factors such as unhealthy lifestyle and obesity representing key targets for future interventions aimed at reducing the impact of this condition on younger generations.

[Identification of Osteoarthritis Inflamm-Aging Biomarkers by Integrating Bioinformatic Analysis and Machine Learning Strategies and the Clinical Validation]. / ç”Ÿç‰©ä¿¡æ¯å­¦å’Œæœºå™¨å­¦ä¹ ç­–ç•¥è¯†åˆ«éª¨å ³èŠ‚ç‚Žç‚Žæ€§è¡°è€ç”Ÿç‰©æ ‡å¿—ç‰©ä¸Žä¸´åºŠéªŒè¯.

Objective: To identify inflamm-aging related biomarkers in osteoarthritis (OA). Methods: Microarray gene profiles of young and aging OA patients were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) were obtained from the Human Aging Genome Resource (HAGR) database. The differentially expressed genes of young OA and older OA patients were screened and then intersected with ARGs to obtain the aging-related genes of OA. Enrichment analysis was performed to reveal the potential mechanisms of aging-related markers in OA. Three machine learning methods were used to identify core senescence markers of OA and the receiver operating characteristic (ROC) curve was used to assess their diagnostic performance. Peripheral blood mononuclear cells were collected from clinical OA patients to verify the expression of senescence-associated secretory phenotype (SASP) factors and senescence markers. Results: A total of 45 senescence-related markers were obtained, which were mainly involved in the regulation of cellular senescence, the cell cycle, inflammatory response, etc. Through the screening with the three machine learning methods, 5 core senescence biomarkers, including FOXO3, MCL1, SIRT3, STAG1, and S100A13, were obtained. A total of 20 cases of normal controls and 40 cases of OA patients, including 20 cases in the young patient group and 20 in the elderly patient group, were enrolled. Compared with those of the young patient group, C-reactive protein (CRP), interleukin (IL)-6, and IL-1ß levels increased and IL-4 levels decreased in the elderly OA patient group (P<0.01); FOXO3, MCL1, and SIRT3 mRNA expression decreased and STAG1 and S100A13 mRNA expression increased (P<0.01). Pearson correlation analysis demonstrated that the selected markers were associated with some indicators, including erythrocyte sedimentation rate (ESR), IL-1ß, IL-4, CRP, and IL-6. The area under the ROC curve of the 5 core aging genes was always greater than 0.8 and the C-index of the calibration curve in the nomogram prediction model was 0.755, which suggested the good calibration ability of the model. Conclusion: FOXO3, MCL1, SIRT3, STAG1, and S100A13 may serve as novel diagnostic biomolecular markers and potential therapeutic targets for OA inflamm-aging.

Quantitative sensory testing in canine musculoskeletal pain: Findings from a systematic review, meta-analysis feasibility assessment, and limitations.

Quantitative sensory testing (QST) allows the study of pain mechanisms, patient phenotyping, and response to therapy. The goals of this study were to conduct a systematic review of the use of QST in dogs with musculoskeletal disease including osteoarthritis (OA), and to assess, by means of a meta-analysis, the ability of QST to differentiate affected dogs from healthy controls. The study protocol was registered; three bibliographic databases were screened. Studies involving QST in healthy dogs and those with musculoskeletal disease were included. Data were extracted using a standardized form. Assessment of quality and risk of bias were performed using the CAMARADES critical assessment tool. Twenty-nine articles met the inclusion criteria [systematic review (n = 11); meta-analysis (n = 28)]. In the systematic review, ten studies performed static QST: mechanical [punctate tactile (n = 6); mechanical pressure (n = 5)]; thermal [cold (n = 3); hot (n = 4)]; electrical (n = 1); and one study performed dynamic QST [conditioned pain modulation (n = 1)]. Most studies were of good scientific quality and showed low to moderate risk of bias. A meta-analysis was not possible due to numerous and severe issues of heterogeneity of data among studies. Methods to reduce risk of bias and use of reporting guidelines are some of the most needed improvements in QST research in dogs. Standardization of QST methodology is urgently needed in future studies to allow for data synthesis and a clear understanding of the sensory phenotype of dogs with and without chronic pain including OA.

Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value. METHODS: OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quantitative analysis of selected ERRGs. RESULTS: We identified four ERRGs associated with OA risk assessment through machine learning methods, and verified the abnormal expressions of these screened markers in OA patients through in vitro experiments. The influence of selected markers on OA immune infiltration was also evaluated. CONCLUSIONS: Our results provide new evidence for the role of ER stress in the OA progression, as well as new markers and potential intervention targets for OA.

Identification of clinical characteristics biomarkers for rheumatoid arthritis through targeted DNA methylation sequencing.

OBJECTIVES: Rheumatoid arthritis (RA) is a complex disease with a challenging diagnosis, especially in seronegative patients. The aim of this study is to investigate whether the methylation sites associated with the overall immune response in RA can assist in clinical diagnosis, using targeted methylation sequencing technology on peripheral venous blood samples. METHODS: The study enrolled 241 RA patients, 30 osteoarthritis patients (OA), and 30 healthy volunteers control (HC). Fifty significant cytosine guanine (CG) sites between undifferentiated arthritis and RA were selected and analyzed using targeted DNA methylation sequencing. Logistic regression models were used to establish diagnostic models for different clinical features of RA, and six machine learning methods (logit model, random forest, support vector machine, adaboost, naive bayes, and learning vector quantization) were used to construct clinical diagnostic models for different subtypes of RA. Least absolute shrinkage and selection operator regression and detrended correspondence analysis were utilized to screen for important CGs. Spearman correlation was used to calculate the correlation coefficient. RESULTS: The study identified 16 important CG sites, including tumor necrosis factort receptor associated factor 5 (TRAF5) (chr1:211500151), mothers against decapentaplegic homolog 3 (SMAD3) (chr15:67357339), tumor endothelial marker 1 (CD248) (chr11:66083766), lysosomal trafficking regulator (LYST) (chr1:235998714), PR domain zinc finger protein 16 (PRDM16) (chr1:3307069), A-kinase anchoring protein 10 (AKAP10) (chr17:19850460), G protein subunit gamma 7 (GNG7) (chr19:2546620), yes1 associated transcriptional regulator (YAP1) (chr11:101980632), PRDM16 (chr1:3163969), histone deacetylase complex subunit sin3a (SIN3A) (chr15:75747445), prenylated rab acceptor protein 2 (ARL6IP5) (chr3:69134502), mitogen-activated protein kinase kinase kinase 4 (MAP3K4) (chr6:161412392), wnt family member 7A (WNT7A) (chr3:13895991), inhibin subunit beta B (INHBB) (chr2:121107018), deoxyribonucleic acid replication helicase/nuclease 2 (DNA2) (chr10:70231628) and chromosome 14 open reading frame 180 (C14orf180) (chr14:105055171). Seven CG sites showed abnormal changes between the three groups (P < 0.05), and 16 CG sites were significantly correlated with common clinical indicators (P < 0.05). Diagnostic models constructed using different CG sites had an area under the receiver operating characteristic curve (AUC) range of 0.64-0.78 for high-level clinical indicators of high clinical value, with specificity ranging from 0.42 to 0.77 and sensitivity ranging from 0.57 to 0.88. The AUC range for low-level clinical indicators of high clinical value was 0.63-0.72, with specificity ranging from 0.48 to 0.74 and sensitivity ranging from 0.72 to 0.88. Diagnostic models constructed using different CG sites showed good overall diagnostic accuracy for the four subtypes of RA, with an accuracy range of 0.61-0.96, a balanced accuracy range of 0.46-0.94, and an AUC range of 0.46-0.94. CONCLUSIONS: This study identified potential clinical diagnostic biomarkers for RA and provided novel insights into the diagnosis and subtyping of RA. The use of targeted deoxyribonucleic acid (DNA) methylation sequencing and machine learning methods for establishing diagnostic models for different clinical features and subtypes of RA is innovative and can improve the accuracy and efficiency of RA diagnosis.

Lessons in clinical reasoning - pitfalls, myths, and pearls: a woman brought to a halt.

OBJECTIVES: Limitations in human cognition commonly result in clinical reasoning failures that can lead to diagnostic errors. A metacognitive structured reflection on what clinical findings fit and/or do not fit with a diagnosis, as well as how discordance of data can help advance the reasoning process, may reduce such errors. CASE PRESENTATION: A 60-year-old woman with Hashimoto thyroiditis, diabetes, and generalized anxiety disorder presented with diffuse arthralgias and myalgias. She had been evaluated by physicians of various specialties and undergone multiple modalities of imaging, as well as a electromyography/nerve conduction study (EMG/NCS), leading to diagnoses of fibromyalgia, osteoarthritis, and lumbosacral plexopathy. Despite treatment for these conditions, she experienced persistent functional decline. The only definitive alleviation of her symptoms identified was in the few days following intra-articular steroid injections for osteoarthritis. On presentation to our institution, she appeared fit with a normal BMI. She was a long-time athlete and had been training consistently until her symptoms began. Prediabetes had been diagnosed the year prior and her A1c progressed despite lifestyle modifications and 10 pounds of intentional weight loss. She reported fatigue, intermittent nausea without emesis, and reduced appetite. Examination revealed intact strength and range of motion in both the shoulders and hips, though testing elicited pain. She had symmetric hyperreflexia as well as a slowed, rigid gait. Autoantibody testing revealed strongly positive serum GAD-65 antibodies which were confirmed in the CSF. A diagnosis of stiff-person syndrome was made. She had an incomplete response to first-line therapy with high-dose benzodiazepines. IVIg was initiated with excellent response and symptom resolution. CONCLUSIONS: Through integrated commentary on the diagnostic reasoning process from clinical reasoning experts, this case underscores the importance of frequent assessment of fit along with explicit explanation of dissonant features in order to avoid misdiagnosis and halt diagnostic inertia. A fishbone diagram is provided to visually demonstrate the major factors that contributed to the diagnostic error. The case discussant demonstrates the power of iterative reasoning, case progression without commitment to a single diagnosis, and the dangers of both explicit and implicit bias. Finally, this case provides clinical teaching points in addition to a pitfall, myth, and pearl specific to overcoming diagnostic inertia.

From biochemical markers to molecular endotypes of osteoarthritis: a review on validated biomarkers.

INTRODUCTION: Osteoarthritis (OA) affects over 500 million people worldwide. OA patients are symptomatically treated, and current therapies exhibit marginal efficacy and frequently carry safety-risks associated with chronic use. No disease-modifying therapies have been approved to date leaving surgical joint replacement as a last resort. To enable effective patient care and successful drug development there is an urgent need to uncover the pathobiological drivers of OA and how these translate into disease endotypes. Endotypes provide a more precise and mechanistic definition of disease subgroups than observable phenotypes, and a panel of tissue- and pathology-specific biochemical markers may uncover treatable endotypes of OA. AREAS COVERED: We have searched PubMed for full-text articles written in English to provide an in-depth narrative review of a panel of validated biochemical markers utilized for endotyping of OA and their association to key OA pathologies. EXPERT OPINION: As utilized in IMI-APPROACH and validated in OAI-FNIH, a panel of biochemical markers may uncover disease subgroups and facilitate the enrichment of treatable molecular endotypes for recruitment in therapeutic clinical trials. Understanding the link between biochemical markers and patient-reported outcomes and treatable endotypes that may respond to given therapies will pave the way for new drug development in OA.