Knee Hurt

Category: Knee Arthritis

  • You Don’t Look Sick – Living with Rheumatoid Arthritis: HAWAII DAY 1

    Well, my start to my trip to Hawaii definitely started with a bang. First I got up early and got to the airport well in time. I had a wonderful taxi driver who was very sweet and he took me and my luggage to the airport lobby and arranged a wheelchair for me. I was so early that I was ahead of the people pushing the wheelchairs to your gate. So I had to sit and wait for about half an hour for someone to push me to my gate.

    Getting through TSA was a lesson in patience. Since I was in a wheelchair, I was wheeled to the front of the line, but then had to wait for a female TSA agent to check my trunk. My boot had to be taken off and put through the X-ray machine and then my sock had to be inspected. I offered to take off my sock and they can take it to the x-ray machine, but they refused and instead felt all over my foot. It seemed kind of stupid. That whole process took about another 45 minutes.

    My flight was very nice. I sat next to two very nice women who both wore masks the entire time and we were super helpful when we landed. In Hawaii the plane lands right on the tarmac and you have to walk down a flight of stairs. The two women carried my backpack and medicine bag so I could hold on to the railing.

    Once I had my luggage, I waited in a long line to get the shuttle to the rental car. When I got to the rental car, I went to the Fast Track thinking I would get my rental car, but unfortunately they claimed I didn’t have my fast track for this reservation. So I had to wait in a long line for him to pick up my rental car. Luckily there were some very nice people in line who held my seat while I sat down for a while. It was very hot and I stood for a long time.

    Finally I get to the front of the line and pick up my rental car. I drove away happily and went to the supermarket to get food. I drove just 20 miles further and it was clear something was wrong with the car. Every time I drove over 60 miles per hour there was a chattering sound like someone was banging in the trunk. After stopping a few times to see if I could see what the problem was, I called roadside assistance. That was an incredibly useless call because the guy kept asking me where I was, but I just knew I was on a highway in the middle of nowhere in Hawaii. So I finally turned around and went back to the airport.

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    Once back at the car rental company at the airport, I saw the same service representative I had spoken to twice. I called him and told him what happened and he quickly brought my new car over.

    When the car came by, I had some really nice people helping me with my bags and groceries. This time the car was great. I arrived at the hotel and the nice lady at the reception helped me with my bags and groceries.

    To give you an idea, I landed at 11:30 AM and arrived at the hotel at 6:00 PM. It took 6.5 hours to get off the plane, pick up my luggage, go to the rental location, get car #1, then a grocery store and drive 20 miles and then drive back and get car #2 and then drive 2 hours to the hotel. It was a lot of standing. Let’s see how my feet feel tomorrow. Oh yeah, I was late for dinner, so I ate snacks.

    See you tomorrow…

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  • Shift towards personalized treatment approaches for chronic inflammatory diseases

    Shift towards personalized treatment approaches for chronic inflammatory diseases

    shutterstock 390538711 6b3c40fdd32742caa54307db3553cab1

    Chronic inflammatory diseases affect 5% to 7% of the population, regardless of age or gender, from children to the elderly. Chronic inflammatory diseases include a range of conditions. The most common inflammatory diseases include rheumatoid arthritis, Crohn’s disease, psoriasis and multiple sclerosis.

    Few of the current therapies for chronic inflammatory diseases are effective and while some can control the disease, they do not provide a cure. To overcome these problems, researchers and physicians are focusing on several topics to improve patient care in therapeutic antibody treatment.

    The landscape of treating chronic inflammatory diseases has undergone a significant transformation with the advent of targeted therapies using therapeutic antibodies. However, a significant proportion of patients do not respond adequately to treatment or experience a decrease in response over time. This challenge mainly relates to issues such as suboptimal dosing, immunogenicity and variations in pharmacokinetics (how the drugs circulate through the body) in different patients.

    In response to this critical need for more effective treatment, researchers have initiated a strategic shift toward personalized treatment approaches. This includes the development of patient stratification tools and the use of therapeutic drug monitoring (TDM) to adjust dosages based on serum drug concentrations.

    The potential for substantial improvement in patient care is enormous by implementing individualized (TDM-guided) dosing regimens of therapeutic antibodies into routine clinical practice for the treatment of chronic inflammatory diseases. This tailored approach will ultimately lead to more efficient use of these valuable but expensive medicines “the right medicine in the right dose for the right patient”.

    However, Europe faces a challenge in the fragmentation of expertise regarding individualized (TDM-guided) treatment optimization. The knowledge and techniques required for effective implementation are concentrated in a limited number of pioneering centers, which makes dissemination to other centers difficult. In particular, the lack of standardization in TDM testing contributes to the complexity.

    Introducing ENOTTA COST promotion

    To address these challenges and promote a more coherent approach, a comprehensive, interdisciplinary pan-European network is being established. ENOTTA COST Action, which stands for “European Network on Optimizing Treatment with Therapeutic Antibodies in chronic inflammatory diseases”,” brings together 156 experts from 31 countries, including key scientific disciplines such as physicians, basic researchers, biologists, computer scientists, pharmacometrists, patients, small and medium-sized enterprises (SMEs) and health authorities, to cover all aspects of the challenge.

    ENOTTA advocates personalized use of therapeutic antibodies to become the new standard of care for patients with chronic inflammatory diseases.”


    Prof. Denis Mulleman, chairman of ENOTTA

    This initiative aims to consolidate and structure scientific research in this area and thus promote collaboration and knowledge exchange. The ultimate goal is to facilitate the seamless integration of individualized (TDM-guided) cost-effective dose optimization of therapeutic antibodies into daily clinical practice for the treatment of chronic inflammatory diseases.

    Added value with ENOTTA

    The ENOTTA COST action is groundbreaking and aims to create a distinctive framework, unlike all existing initiatives. This innovative initiative is designed to facilitate networking, sustainable collaboration and expansion of partnerships between participants across Europe.

    ENOTTA stands ready to catalyze progress in this therapeutic area, facilitating the exchange of expertise and the wide dissemination of valuable knowledge for the benefit of patients suffering from long-standing inflammatory diseases. By fostering scientific collaboration between key players in the European research landscape, this initiative will play a crucial role in advancing the field of personalized use of therapeutic antibodies. Furthermore, it will establish critical connections with leading experts on a global scale.

    Taken together, the innovative nature of this action will come from the ability of the entire group to create tools for patient stratification, test harmonization, universal standards and the availability of guidelines and treatment algorithms that are accepted by health insurers, physicians and patients. . These developments will be crucial for implementing individualized (TDM-based) dose optimization of therapeutic antibodies in daily clinical practice. Ultimately, the optimal use of therapeutic antibodies using TDM will alleviate the burden on the healthcare system. Furthermore, the insights gained from this action can also guide future research in other disciplines, such as oncology, metabolic diseases and cardiovascular diseases, using therapeutic antibodies.

    Source:

    European cooperation in science and technology (COST)

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  • YKL-40 serum levels are predicted by inflammatory state, age, and diagnosis of idiopathic inflammatory myopathies

    In this study, YKL-40 serum levels are influenced by factors such as age, inflammation and diagnosis of autoimmune diseases (RA/MII)3. Currently, due to its participation in tissue remodeling and degradation, attempts have been made to use it as a biomarker in pro-inflammatory states and as an indicator of poor prognosis in inflammatory diseases.6.7. However, its usefulness is still controversial because its full biological effects are still unknown. Furthermore, the specific factors that promote its expression, as well as its interaction with the majority of cytokines and molecules involved in the development and establishment of autoimmune inflammatory diseases, are not well established.17.

    YKL-40 serum levels have been reported to increase with age in various cardiovascular, metabolic and systemic inflammatory diseases3. Bojesen et al. found that serum levels of YKL-40 increased exponentially with aging. In subjects with two YKL-40 measurements ten years apart, the average increase in YKL-40 was 1.5 μg/l/year18. Regarding inflammatory diseases, increased serum levels of YKL-40 were reported in anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, which was hypothesized to play a role in promoting chemotaxis, tissue damage and vascular damage.19.

    In RA it has been recognized as a potential candidate autoantigen. Furthermore, in these patients it is produced and secreted by monocytes differentiated into macrophages, articular chondrocytes, synovium, peripheral blood mononuclear cells (PBMCs), and endothelium.17,19,20. It has been proposed that the pathogenic mechanism of YKL-40 in RA initiates through its binding to the HLA-DR4 peptide-binding motif promoting mononuclear cell proliferation, and HLA-DM plays a key role in presenting YKL-40 to CD4+ T cells. Furthermore, antigen-presenting cells (APCs) present YKL-40 at early-stage RA sites, suggesting an association for YKL-40 in the pathogenesis of RA.20,21,22. On the other hand, differentiated DR4+ dendritic cells and macrophages are similar to APCs of synovial joints and have the potential to carry out MHC II presentation of YKL-40 epitopes, resulting in higher levels in synovial and serum.21,22. Although the pathogenic mechanism in RA has been elucidated, agents that promote YKL-40 expression in RA are still lacking. It has been associated with the development of chronic, destructive, relapsing arthritis due to its role in tissue remodeling and breakdown. . It is considered an effective marker in estimating RA disease activities and prognostic value, and may be a therapeutic target19.

    As for IIM, information is even more limited as few studies have been conducted in this regard and thus the role YKL-40 plays in this area has not yet been established. Regarding the pro-inflammatory effect and its relationship with the diagnosis and phenotype of IIM, Noguchi et al. found significantly increased serum in patients with PM/DM compared to the healthy population, as well as age-adjusted serum YKL-40 levels were significantly increased in patients with PM/DM compared to HC. In muscle biopsies, infiltration of YKL-40-positive inflammatory cells (probably macrophages) in the endomysium and perimysium was found. This suggests that cells other than CD8+ and CD4+ T cells can cause inflammation.23.

    Ming-Zhu Gao et al. measured YKL-40 levels in patients with DM/PM and HI and reported significantly higher levels in patients with IIM compared to controls (51.6 vs. 27.8 ng/ml, respectively)6. In a systematic review by Cui et al. reports levels of 84.09 ng/ml in patients with PM and DM versus 27.37 ng/ml in HI24. On the other hand, Carboni et al. analyzed YKL-40 serum levels and its expression in muscle tissue in patients with ASSD. However, serum levels of YKL-40 did not correlate with other clinical, laboratory, disease status, or therapeutic parameters. Furthermore, YKL-40 was expressed by the inflammatory cells of the muscle tissue.13. Our study reinforces these results with serum levels of 187.80 ng/ml in patients with IIM versus 46.82 ng/ml in RA and 57.17 ng/ml in HI, as well as their presence mainly in inflammatory cells. This increase can be explained by inflammation, activation of macrophages, destruction of fibroblasts and existing vascular changes.

    As previously mentioned, several biological effects of YKL-40 are known, such as inflammation and tissue remodeling, as well as its main sources, which, however, highlights its exacerbated expression in inflammatory diseases such as RA or SLE; some researchers have pointed out that this expression may vary depending on the disease type, which could be due to the multi-organ damage in IIM compared to RA, where the damage is mainly directed against the joints19.24. Tang et al. found that YKL-40 concentration was significantly higher in IIM patients with myocardial injury than without myocardial injury25.

    In addition, YKL-40 plays a role in cardiovascular diseases such as early atherosclerosis, essential hypertension and other progressive vascular complications. In IIM patients, as in many other autoimmune diseases, serum levels of this protein have a positive association, particularly with atherosclerosis, and may predict both overall and cardiovascular mortality.5,26,27.

    We wanted to know whether YKL-40 is affected by certain factors in IIM, as mentioned by Tizaoui et al. First, we compared some demographic, laboratory and clinical variables between patients with RA and IIM mentioned in Table 1. Of all After analyzing the variants, we found a significant difference in pDBP (P = 0.024) and pMBP (P = 0.035) which were higher in IIM patients. This may be due to the fact that blood vessels suffer damage in the early stages of the development of inflammatory diseases, altering blood pressure and increasing the risk of cardiovascular damage.28.29. Furthermore, the endothelial changes in IIM progress to microangiopathy, causing blood pressure changes12. Although information on exactly how the process occurs is scarce, it has been suggested that cardiovascular disease and cardiovascular risk increase mortality in IIM patients, but this is becoming controversial because a single center cross-sectional study recently reported that it risk of cardiovascular disease increases. factors in IIM patients are not significant compared to HI, but are significant in IIM if they are related to age, disease duration, duration of therapy and body composition, which could be related to our patients included in our study25.30. Although these evaluated variables were significant between these two groups, serum levels of YKL-40 were the most significant variable (P = 0.010). These were higher in IIM than in RA patients (187.80 ng/ml). vs 46.82 ng/ml, respectively).

    Once we established that IIM exhibits higher serum levels of YKL-40 than RA, we examined whether serum levels of YKL-40 were influenced by age or disease duration. Our results show that only aging has a positive correlation with increased YKL-40, but not with disease duration. The reports conducted by Johansen in 2006 and Schultz 2010 have clearly shown that aging is predictive of the increase of YKL-40 in HI, but in our IIM patients the concentration of this protein is higher due to inflammation and damage to multiple organs.2.3.

    We evaluated the predictive value of inflammatory state, age and diagnosis of IIM on serum levels of YKL-40 and clarified that CRP has predictive value on serum YKL-40 levels in IIM patients.P= 0.038) which corresponds to a cross-sectional survey and systematic review published by Cui andYou at the . In addition, age and the IIM diagnosis ( P=0.008AndP=0.001respectively) were found to be powerful predictors of YKL-40 serum levels24. On the other hand, we confirmed that age and IIM diagnosis have an important influence on YKL-40 concentration, because YKL-40 serum levels are the highest compared to control and RA groups, thus we know that the presence of the disease or its type influences the disease. the YKL-40 concentration. Some reports state that the YKL-40 concentration in HI is stable for many years, but increases with aging or inflammatory conditions. Other researchers reported that normal YKL-40 serum levels may be different among a healthy population. Therefore, they recommend establishing baseline values ​​for each study, because in addition to environmental factors, genetic load is another variable that can influence the expression pattern. of this protein26. We previously mentioned the pathogenic mechanism by which YKL-40 expression is mediated in RA and its possible role in IIM, but the information is still insufficient.

    Regarding the in situ analysis of YKL-40 muscle expression, we observed that YKL-40 is mainly expressed in inflammatory cells rather than in muscle cells. We also observed that YKL-40 expression is associated with higher CPK serum levels and MYOACT score which are often related. to higher inflammation and muscle weakness. This observation is in agreement with the unique previous report of YKL-40 in muscle tissue made by Carboni et al.13It thus supports its role in inflammation, as well as its function as a clinical marker for poor prognosis in inflammatory diseases.

    Taking into account all our findings, we demonstrated the expression of YKL-40 in both HI and in patients with RA and IIM and the possible factors that could influence its expression.

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  • New exercise test aims to improve the quality of life of people with rheumatoid arthritis

    New exercise test aims to improve the quality of life of people with rheumatoid arthritis

    shutterstock 390538711 6b3c40fdd32742caa54307db3553cab1

    Rheumatoid arthritis is an inflammatory disease that can cause severe pain and swelling of the joints. But a new exercise intervention could help improve physical function and quality of life in people struggling with this debilitating condition.

    In a new trial, researchers from the University of South Australia are working with Arthritis SA to investigate the potential of Blood Flow Restriction (BFR) training to improve the strength and mobility of people with rheumatoid arthritis.

    BFR training is an exercise technique in which people wear pressurized bands – much like blood pressure cuffs – to slow blood flow to the muscles while they exercise. The cuff allows blood flow to the limb but slows its outflow, developing muscle strength without the need for heavy weights.

    In Australia, rheumatoid arthritis is the second most common form of arthritis, affecting more than 450,000 people. More than 18 million people worldwide live with a condition. Women are two to three times more likely to develop rheumatoid arthritis than men.

    Sports scientist Dr. UniSA’s Hunter Bennett says the research hopes to identify interventions that can improve the quality of life for people with rheumatoid arthritis.

    “Rheumatoid arthritis can be a particularly debilitating disease. It is caused by the immune system attacking healthy tissues, leading to pain and swelling, joint destruction and loss of muscle mass and strength,” says Dr. Bennett.

    Although medications can reduce symptoms, they do not address the loss of muscle strength and function.

    The best way to increase strength and combat muscle loss is through resistance training, but this is often problematic for people with rheumatoid arthritis due to pain, fatigue or risk of injury.

    Blood flow restriction training (BFR) offers an alternative. BRF is used in many sports and rehabilitation settings in Australia and is considered a safe and effective method for improving strength and function in many clinical populations, including people with osteoarthritis.

    Because this technique uses very low loads, it is a viable option for people with rheumatoid arthritis. So in our research we look at how BRF can increase people’s strength and hopefully increase their freedom of movement and overall well-being.”

    Dr. Hunter Bennett, exercise scientist, UniSA

    The research team is currently seeking expressions of interest from women and men aged 45 to 75 years diagnosed with rheumatoid arthritis.

    Exercise intervention eases pain for people with rheumatoid arthritis

    Video credit: University of South Australia

    Source:

    University of South Australia

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  • JAK inhibitors provide effective and convenient treatment options for arthritis patients

    JAK inhibitors provide effective and convenient treatment options for arthritis patients

    Arthritis

    According to a new article in Rheumatology, published by Oxford University Press, JAK inhibitors, which doctors have used to treat patients with arthritis despite concerns about the effectiveness of such drugs, actually work quite well. In a multicenter retrospective study, Japanese researchers found that the drugs resulted in impressive remission rates in patients, most of whom choose to continue such treatment.

    Rheumatoid arthritis is a common autoimmune disease characterized by chronic inflammation of the joint linings and results in progressive joint destruction and other systemic complications. The use of biologic disease-modifying drugs allows patients to enjoy achieving low disease activity and remission. But clinics must administer such medications via subcutaneous or intravenous routes, which is unpleasant for patients, and over time these medications often become less effective.

    Recently, scientists have developed Janus kinase (JAK) inhibitors for the treatment of arthritis. Patients take such medications orally. Previous research has demonstrated the efficacy and safety of JAK inhibitors in randomized controlled trials. However, some researchers have questioned the potential efficacy of JAK inhibitors for widespread use by patients. In practice, doctors usually treat patients with JAK inhibitors, precisely because these patients have other health problems and conventional medications such as methotrexate are less effective for them. Real-world patients have distinctive characteristics compared to those recruited in randomized controlled trials.

    In the current multicenter retrospective study, using data from 622 patients treated at seven major university hospitals in Japan, researchers compared the efficacy and safety of four common JAK inhibitors: tofacitinib, baricitinib, peficitinib, and upadacitinib.

    The researchers here found that about one in three patients achieved remission, and three in four achieved at least low disease activity, with both figures representing impressive efficacy. They noted that more than 80% of patients were still taking the JAK inhibitors after six months.

    They believe that this is especially relevant because with these oral medications there cannot be a failure of immunological secondary treatment, where medications are no longer effective because they cause adverse immune system responses in patients. Failure of secondary immunological treatment is common in patients treating their arthritis with drugs such as methotrexate.

    Source:

    Oxford University Press USA

    Magazine reference:

    Hayashi, S., et al. (2023) Real-world comparative study of the efficacy of Janus kinase inhibitors in patients with rheumatoid arthritis: the ANSWER cohort study. Rheumatology. doi.org/10.1093/rheumatology/kead543.

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  • You Don’t Look Sick – Living With Rheumatoid Arthritis: ANOTHER SET OF X-RAYS?

    Today I got up early to go to the foot doctor again. She checked my foot and then sent me down for x-rays. This is my fourth set of x-rays in a month. It turned out that my right foot was not broken a second time. I must have just messed it up. She says my left ligament is still wobbly.

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    So the first thing she did was tell me to exchange my boots. The large boot is now on a break on my right foot and the Birkenstock type boot has been retired. I now wear the night cloth on my left foot all day and at night. She said I can put my left foot with the cuff in a pair of sturdy shoes or sneakers with a hard bottom. Well, it just so happens that my new Inka sneakers have a hard bottom. So when I go outside, I can put my left foot in a sneaker. I also put my left foot with the cloth in my Oofas.

    It’s only been 10 hours and my right foot already feels better. Being protected in the big boot makes such a difference.

    So yesterday I went out and my car battery wouldn’t turn my car on. The battery was not completely empty, but just not strong enough. Today my neighbor came by with a battery starting battery and his trickle battery. After about 20 minutes the car started and I stood outside and let it run for 30 minutes. Luckily a neighbor stopped by to chat with me while I was standing there. I would have sat down, but the trunk doesn’t really fit in the driver’s seat. Now I have to start the car every day this week to charge the battery.

    See you tomorrow..

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  • Anti-TNF-α therapy induced psoriasis in patients with rheumatoid arthritis according to FDA postmarketing surveillance data

    FDA Adverse Event Reporting System (FAERS)

    The United States Food and Drug Administration Adverse Event Reporting System (FAERS) is a data repository that collects voluntary drug-related reports from healthcare professionals, consumers, and legal representatives. In cases where the adverse reaction (AE) is reported to the manufacturer, the manufacturer is required to forward the report to FAERS. At the time of the survey, FAERS contained 17,392,666 AE reports collected from the first quarter of 2004 (including historical reports since 1982) through the second quarter of 2022. The reports are available online: https://www.fda.gov/ medications/questions-and-answers-fdas-reporting-system-faers-side-effects/fda-reporting-system-faers-last-quarter-data.

    Data preparation

    FAERS reports are added quarterly and stored in a set of text files. Subsets of data are organized by specific report fields (demographics, drug, side effects, outcome, etc.) and their respective case IDs. The data format is not uniform and has changed several times since its inception. Therefore, appropriate changes have been made. Moreover, as the side effect reports are collected from all over the world, the respective brand names of drugs are translated into the generic equivalents19,20,21.

    Study results

    The MedDRA Dictionary version 25.1 was searched to define the measured study outcomes using higher-level terms such as “immune-associated conditions not elsewhere classified (NEC)” and “psoriatic conditions.” All psoriasis-associated preferred terms (PT) were used in the search. To avoid indication-related confounding effects, psoriatic conditions associated with RA, such as psoriatic arthropathy, were excluded from the MedDRA PT list. The following PTs were used for the definition psoriasis in the analysis: erythrodermic psoriasis, guttate psoriasis, nail psoriasis, psoriasiform dermatitis, pustular psoriasis and psoriasis.

    Cohort selection

    Of the total 17,392,666 adverse event reports in FAERS, a total of 881,182 reports included RA indications, and for 663,922 of these, RA was listed as the sole indication. These data were further broken down by monotherapies and only reports from physicians, pharmacists and other healthcare professionals were included to avoid bias and increase clinical relevance. The final monoindication + monotherapy sets were as follows: certolizumab pegol (n = 5168), adalimumab (n = 9221), golimumab (n = 2899), tocilizumab (n = 4819), abatacept (n = 7574), infliximab (n = 5579), rituximab (n = 2519), etanercept (n = 89543), tofacitinib (n = 10686), and methotrexate (n = 6142). Demographic analysis was performed for TNF inhibitors and methotrexate RA AE cohorts (Tables 1 and 2). The following terms for psoriasis are included: erythrodermic psoriasis, guttate psoriasis, nail psoriasis, psoriasiform dermatitis, pustular psoriasis and psoriasis. These psoriasis type terms describe the psoriasis Adverse event rates were calculated for each drug cohort: certolizumab pegol (n = 98), adalimumab (n = 107), golimumab (n = 20), tocilizumab (n = 29), abatacept (n = 40), infliximab (n = 29 ), rituximab (n = 11), etanercept (n = 260), tofacitinib (n = 24), and methotrexate (n = 7). A disproportionality analysis was performed using the reported AE rates to calculate the reporting odds ratios (RORs). These figures were used to calculate psoriasis reported frequencies. Methotrexate was selected as a control cohort because of its unique mechanism of action (MOA) as an immunosuppressant that inhibits the conversion of folic acid to folic acid cofactors, and because of its common use as a monotherapy in RA.

    Demographic analysis

    Gender (Table 1).

    Table 1 The total number of reports of TNF inhibitors and methotrexate in the combined RA cohorts, separated by reported gender.

    Age (Table 2).

    Table 2 The total number of reports of TNF inhibitors and methotrexate in the combined RA cohorts, separated by reported age.

    static analysis

    Descriptive statistics

    The frequencies for each side effect examined (Figs. 1, 3) were calculated using the following equation:

    $$\textFrequency = \left( \textnReports\,\text with \,\textpsoriasis \,\textin \, \texta \,\textcohort \right)/\textnReports\,\text in \,\texta \,\ textcohort*100$$

    (1)

    Frequency error:

    $$\textError = \left( \sqrt \textnReports \,\textwith\, \textpsoriasis\,\text in \,\text a\, \textcohort \right)/\textnReports\,\text in\,\text a \,\textcohort*100$$

    (2)

    Comparative statistics

    The numbers of psoriasis reports were compared via the Reporting Odds Ratio (ROR) analysis for Fig. 2, 4 and 5 and Tables 3, 4 and 5 using the following equations:

    $$\mathrmROR=(\mathrma/\mathrmb)/(\mathrmc/\mathrmd)$$

    (3)

    where Number of cases in exposed group with psoriasis, Number of cases in exposed group without psoriasis, Number of cases in control group with psoriasis, Number of cases in control group without psoriasis.

    Table 3 RORs and 95% CIs were calculated based on comparisons between each TNF inhibitor monotherapy cohort and the methotrexate cohort.
    Table 4 RORs and 95% CIs were calculated based on comparisons between each monotherapy cohort examined and the methotrexate cohort.
    Table 5 RORs and 95% CIs were calculated based on comparisons between the certolizumab pegol monotherapy cohort and each of the other monotherapy cohorts studied.

    $$\mathrmLnROR=\mathrmLn(\mathrmROR)$$

    (4)

    Standard error of odds ratio for log reporting;

    $$\mathrmSE_\mathrmLnROR=\sqrt1/\mathrma+1/\mathrmb+1/\mathrmc+1/\mathrm d$$

    (5)

    95% confidence interval;

    $$95\text\%CI = \left[ {\textexp\left( \textLnROR – 1.96 \times \textSE_\textLnROR \right),\textexp\left( {\textLnROR + 1.96 \times \textSE_{\textLnROR} } \right)} \right]$$

    (6)

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  • Epidemiology and clinical features of interstitial lung disease in patients with rheumatoid arthritis from the JointMan database

    Data source

    Patient demographics and disease characteristics were retrospectively analyzed after data extraction from the Discus Analytics JointMan database, a large US electronic health record-based dataset initiated in March 2009. The JointMan database includes > 17,000 rheumatology patients covered by commercial, Medicare, or Medicaid insurance. plan. Practices in the following eight states are included: Washington, New York, Oregon, Florida, Georgia, California, Wisconsin and Kentucky. Patient data were collected in rheumatology centers and anonymized prior to analysis. In addition to electronic medical record data, the JointMan user interface collects clinical results recorded by physicians at the time of the encounter.

    Patient population

    Patients were included if they were ≥ 18 years old at first visit to a rheumatologist participating in the JointMan network, had a provider-selected diagnosis of RA between January 1, 2009 and September 20, 2019, and had ≥ 1 visit after the first visit. visit date. Patients were excluded if their first encounter occurred after RA diagnosis or if they experienced a drug-induced ILD diagnosis [International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes J70.2 and J70.4] at any time during the study period. Patients were assigned to the RA cohort (patients with confirmed RA but no diagnosis of ILD during the study period) or the RA-ILD cohort (patients with a diagnosis of unmedicated ILD on or after the initial diagnosis date of RA). ). The RA index date was defined as the first RA diagnosis date recorded in the JointMan database (provided by the rheumatologist).

    The total study population consisted of patients followed from the day after the RA index date until the patient’s last encounter date or the end of the study (September 20, 2019), whichever came first. RA was diagnosed according to the ICD, Ninth Revision, CM (ICD-9-CM) code 714.0 and ICD-10-CM codes M05 and M06. ILD was identified by ICD diagnosis codes (ICD-9-CM codes: 516.0, 516.2, 516.3, 516.4, 516.5, 516.8, and 516.9; ICD-10-CM codes: J84.0, J84.1, J84.2, J84 .81, J84.82, J84.83, J84.89 and J84.9) or as indicated by the provider.

    A subanalysis was performed on a series of patients grouped according to ILD diagnosis. For the subanalysis population, the ILD diagnosis index was defined as the first date of ILD diagnosis recorded in the JointMan database (for patients in the RA-ILD cohort), and patient characteristics were described for the 90-day periods before and after the ILD. diagnosis index. For patients without ILD, the index date was based on the distribution of the number of days between RA diagnosis and ILD diagnosis in the RA-ILD cohort; characteristics were described for the 90-day periods before and after the index date (Supplementary Figure S1).

    Primary endpoints

    The primary endpoints, assessed in the total study population, were the prevalence and time to onset of ILD. Prevalence was defined as the proportion of patients with RA and a diagnosis of ILD divided by the total number of patients with RA during the study period. Time to onset of ILD was defined as the time from the initial diagnosis of RA to the first observed non-drug ILD diagnosis.

    Exploratory endpoints

    Exploratory endpoints, assessed in the exploratory analysis population, included baseline demographics, comorbidities, RA characteristics, and overall RA disease activity in the RA cohort compared with the RA-ILD cohort. RA features include joint stiffness, erosions, extra-articular disease, anti-CCP antibodies, joint swelling, ESR, C-reactive protein (CRP), and Clinical Disease Activity Index (CDAI). The CDAI remission score was defined as ≤ 2.8; CDAI low, moderate, and high disease activity scores were defined as >2.8–10, >10–22, and >22, respectively19. The Simplified Disease Activity Index (SDAI) remission score was defined as ≤ 3.3; SDAI low, moderate, and high disease activity scores were defined as > 3.3 to 11, > 11 to 26, and > 26, respectively19. Disease activity score in 28 joints using CRP (DAS28 [CRP]) remission score was defined as ≤ 2.3; DAS28 (CRP) low, moderate and high disease activity scores were defined as > 2.3 to 2.7, > 2.7 to < 4.1 and ≥ 4.1, respectively20. DAS28 (ESR) remission score was defined as <2.6; DAS28 (ESR) low, moderate, and high disease activity scores were defined as 2.6 to 2.6, respectively < 3,2, 3,2–5,1 en > 5.1.19 Routine Assessment of Patient Index Data 3 (RAPID3) remission score was defined as ≤ 3; RAPID3 low, moderate, and high disease activity scores were defined as >3 to 6, >6 to 12, and >12, respectively21. Variables were assessed as potential predictors of RA-ILD.

    Subanalysis endpoints

    For patients included in the subanalysis population, CDAI and RAPID3 scores, number of swollen and swollen28 joints, number of encounters with rheumatologists, and treatment use before and after the ILD diagnosis index were also assessed. The number of swollen and swollen28 joints is part of the DAS/DAS28 score: the number of swollen joints is an assessment of 28 or more (maximum 44) joints, while the number of swollen28 joints is an assessment of only 28 pre-selected joints22.

    static analysis

    The prevalence (95% confidence intervals [CIs]) of the first observed ILD diagnosis during follow-up was calculated. Time to ILD diagnosis was examined using unadjusted Kaplan-Meier survival curves. Descriptive statistics for continuous baseline variables were compared using Student’s Ttest and percentages for categorical and binary basic variables were compared using the Chi-square test.

    Potential predictors of RA-ILD were analyzed with a Cox regression model. Patient demographics and comorbidities were collected at baseline and controlled in the Cox model. RA features were identified during and after initial RA diagnosis and were controlled as time-varying covariates in the Cox model. The final covariate lists were based on clinical rationale and model fit; Hazard Ratios, 95% Confidence Intervals, and P Values ​​were provided for each covariate. Statistical significance for model inclusion was set at P<0.05.

    The number and percentage of patients with visits to a rheumatologist, treatment utilization, and each disease activity score in the pre- and post-index periods were calculated. P-values ​​for the disease activity score category compared pre- and post-index periods and correspond to Fisher’s exact test or Chi-square test with statistical significance set at P<0.05.

    Ethical approval

    This study was conducted in accordance with the International Society for Pharmacoepidemiology Guidelines for Good Pharmacoepidemiology Practices and applicable regulatory requirements23. The study protocol was reviewed by the internal BMS Observational Protocol Review Committee (OPRC). No identifiable protected health information was retrieved from or accessed from the database during the study. Therefore, the BMS OPRC confirmed that this analysis did not require ethical oversight. In addition, the study did not involve the collection, use, or transmission of individually identifiable data, and the data was collected in the setting for the patient’s usual care. Informed consent from the study participants was not required because the dataset used in this observational study consisted of anonymized secondary data released for research purposes.

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  • Fibroblast multiplicity in RA: a synovial state of the art

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  • Relevance of circulating semaphorin 4A to rheumatoid arthritis response to treatment

    Analysis of cohort 1 from Cochin Hospital, Paris

    Study population

    Between May 2016 and February 2018, a total of 101 patients (85 women, 84%) with established RA were included. These patients had a mean age of 58 ± 13 years, a mean disease duration of 14 ± 11 years, and a mean follow-up age. -from 41 ± 15 months. Positive rheumatoid factors and anti-CCP antibodies were detected in 80 (79%) and 83 (82%) patients, respectively. Erosions were present in 63 (62%) patients; 70 patients (69%) received corticosteroids (including 9 at a dose > 10 mg/day), 78 received conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs), including 61 (60%) with MTX, and 59 (58%) received targeted biological DMARDs (bDMARDs). During the inclusion visit, 13 patients initiated a first-line bDMARD or switched to another bDMARD due to inadequate disease control. Detailed characteristics of our study sample are shown in Table 1.

    Table 1 Characteristics of patients from the two cohorts at baseline.

    Results

    The number of annual consecutive visits ranged from 2 to 5 (88 patients with 3 visits, 72 with 4 visits, and 65 with 5 visits). Disease flares occurred in 38 patients during the mean follow-up period of 41 ± 15 months. Of these 38 patients, targeted therapy was added or modified in 26 patients due to inadequate disease control: 10 started on a bDMARD or a targeted synthetic (ts)-DMARD and 16 switched from a bDMARD to a new b- or tsDMARD (Table S1) . The mean time to treatment adaptation was 35 ± 13 months.

    Primary endpoint: evaluation of the predictive value of SEMA4A for the occurrence of treatment failure

    Baseline SEMA4A levels > 94 ng/ml were predictive of treatment failure, defined by the occurrence of flares AND treatment escalation (n = 26 patients), with an HR of 2.73 (95% CI 1.24 –5.96) (Fig. 1A). Results were unchanged after excluding the 13 patients with active disease at baseline who requested addition or change to a bDMARD (HR: 2.83, 95% CI 1.14–7.52).

    Figure 1
    Figure 1

    Predictive value of SEMA4A for the progression of rheumatoid arthritis in Paris cohort 1. (a) Time to treatment failure (defined as flares AND treatment escalation) according to circulating SEMA4A concentrations (≤ or > 94 ng/ml). (b) Survival without disease flare according to circulating SEMA4A concentrations (≤ or > 94 ng/ml).

    Secondary endpoints

    Elevated SEMA4A levels (>94 ng/ml) at baseline were predictive of flare occurrence (n = 34 patients) during the follow-up period (Fig. 1B) with a hazard ratio (HR) of 2.43 (95% confidence interval ). , CI 1.27–4.68). Results were unchanged after excluding the 13 patients with active disease at baseline (HR 2.36, 95% CI 1.15–4.89).

    Baseline SEMA4A concentrations were significantly increased in patients who experienced flares during the follow-up period (78 ± 30 ng/ml vs. 60 ± 24 ng/ml, p < 0.001) (Fig. 2A). SEMA4A levels were also significantly higher in the 13 patients with active disease at baseline who requested the addition or modification of a bDMARD, compared with the 88 patients on stable treatment (84 ± 33 ng/ml vs. 63 ± 26, p = 0.011). Although baseline SEMA4A concentrations were higher in patients experiencing flares AND treatment escalation compared to those on stable treatment (75 ± 31 ng/ml vs. 63 ± 26 ng/ml, p = 0.060), this did not reach significance (Fig. 2B). Patients with elevated SEMA4A levels at baseline maintained higher DAS28 levels throughout the follow-up period, with significant differences at visits 1, 2, and 5 (Fig. 2C).

    Figure 2
    Figure 2

    Baseline circulating SEMA4A levels according to the occurrence of (a) disease flare or (b) treatment failure (defined as flares AND treatment escalation) during the prospective follow-up period in Paris cohort 1. (c) Course of the DAS28 during the follow-up period according to baseline SEMA4A concentrations (≤ or > 94 ng/ml). All data are presented as the mean ± SEM. *p < 0.05, **p < 0.01 and ***p < 0.001, determined by Student's t-test.

    Integration of SEMA4A with other predictors of treatment failure

    A baseline DAS28 > 3.2 (HR 2.17, 95% CI 1.01–4.72) and the presence of active synovitis, defined by at least grade 2 Doppler activity8, detected at at least one joint on power Doppler ultrasound (PDUS) (HR 3.60, 95% CI 1.07–12.15) were predictive of further treatment failure. These results were not changed after excluding the 13 patients with active disease at baseline.

    Baseline age, disease duration, ACPA or RF positivity, smoking status, presence of erosions, series of targeted DMARDs, corticosteroid treatment, and CRP levels were not predictive of treatment failure (Table 2). Multivariate Cox analyzes adjusted for these covariates confirmed that SEMA4A was the only independent predictor of treatment failure (HR 2.71, 95% CI 1.14–6.43).

    Table 2 Univariate and multivariate Cox analyzes to identify independent predictors of treatment failure (primary endpoint) and RA flares (secondary endpoint) in Paris cohort 1.

    SEM4A was also confirmed as an independent predictor of flares, along with DAS28 and synovial hyperhemia (Table 2).

    We then assessed the possible combination of DAS28, PDUS and SEMA4A concentrations to predict the occurrence of treatment failure and flares (Table 3). The combination that provided the best predictive value was a DAS28 > 3.2 and/or presence of active synovitis on PDUS and/or SEMA4A concentrations > 94 ng/ml (HR 10.42, 95% CI 1.41–76 .94 for treatment failure and 4.88, 95% CI 1.50–15.89 for flares) (Fig. 3A,B). Matrix models also highlighted the ability of the combination of these 3 parameters to predict the occurrence of treatment failure and flares (Fig. S1): Treatment failure and flares of RA occurred in 53% and 73% of patients with DAS28 > 3.2 at baseline and the presence of active synovitis at PDUS and SEMA4A concentrations > 94 ng/ml, respectively. Furthermore, only one patient with a DAS28 ≤ 3.2, no active synovitis and SEMA4A ≤ 94 ng/ml experienced treatment failure and RA attacks.

    Table 3 Predictive value of circulating SEMA4A alone or in combination DAS28-CRP and/or active synovitis on power Doppler ultrasound for the occurrence of treatment failure (primary endpoint) and RA attacks (secondary endpoint) in Paris cohort 1.
    figure 3
    figure 3

    Predictive value of SEMA4A, alone or in combination with a Disease Activity Score (DAS) 28 > 3.2 and/or the presence of active synovitis on power Doppler ultrasound (PDUS) in cohort 1 from Paris. (a) Time to treatment failure (defined as flares AND escalation of treatment) according to circulating SEMA4A concentrations (> 94 ng/ml) and/or a DAS28 > 3.2 and/or the presence of active synovitis on PDUS. (b) Survival without disease flare according to circulating SEMA4A concentrations (> 94 ng/ml) and/or a DAS28 > 3.2 and/or the presence of active synovitis on PDUS.

    Predictive value of SEMA4A in the subgroup of patients with low disease activity or remission

    Among the 58 patients with a DAS28 < 3.2 at baseline, treatment failed in 11 (19%) patients during the observation period. In this population, increased SEMA4A concentration was the only variable predicting the occurrence of treatment failure (HR 3.50, 95% CI 1.02–12.01). The presence of active synovitis detected on at least one joint on PDUS and other clinical or biological variables did not predict treatment failure (Table S2).

    In the 37 patients with a DAS28 < 2.6, treatment failure occurred in 4 patients (11%) and elevated SEMA 4A showed a trend for predicting treatment failure (HR 3.30, 95% CI 0.82–152.11, p = 0.069).

    Elevated SEMA4A concentration was also identified as the only predictor of flares (n = 16, 28%) in this subgroup of 58 patients with DAS28 < 3.2 (HR 3.68, 95% CI 1.33–10.17 ).

    Analysis of cohort 2 from Pelegrin Hospital, Bordeaux

    Study population

    A total of 40 patients (29 women, 73%) were included. These patients had a mean age of 57 ± 14 years, a mean disease duration of 5 ± 6 years, and active disease with a mean DAS28 of 5.12 ± 1.40. Positive rheumatoid factors and anti-CCP antibodies were detected in 27 (79%) and 28 (82%) patients, respectively. Erosions were present in 16 (40%) patients; 26 patients (65%) received corticosteroids. During the inclusion visit, 15 patients started MTX as first-line treatment and 25 started tocilizumab. Tocilizumab initiators were older, had longer disease duration and disease activity, and received corticosteroids more often than MTX initiators. Detailed characteristics of our study sample are given in Tables 1 and S3.

    Analysis of the course of SEMA4A serum levels according to response to treatment

    Of the 40 patients included, 4 experienced no response to treatment, 10 had a moderate response and 26 had a good response. As previously observed, baseline SEMA4A levels correlated with the DAS28 (r = 0.29, p = 0.038) and a trend was observed with CRP (r = 0.26, p = 0.10). At month 3, SEMA4A concentrations correlated with DAS28 and CRP (r = 0.31, p = 0.029 and r = 0.38, p = 0.017, respectively). Furthermore, baseline SEMA4A concentrations were significantly increased in active patients at inclusion, defined by a DAS28 > 3.2 (Fig. S2A). Interestingly, baseline SEMA4A levels were significantly higher in patients who otherwise experienced no or moderate response (198 ± 30 ng/ml) compared to patients with a good response (176 ± 24 ng/ml, p = 0.035) (Fig. S2B ). It was found that serum SEMA4A levels decreased significantly between m0 and m3, especially in the group of patients with good clinical response (Fig. S2C). This result was observed in the subgroups of patients starting MTX or tocilizumab (Fig. S2D,E).

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