The 16-week Plants for Joints trial investigated the effects of a multidisciplinary lifestyle intervention in people with RA compared to usual care. The intervention was based on a whole-food, plant-based diet alongside physical activity and stress management. Previous reports showed this intervention significantly reduced the 28-joint disease activity score (DAS28) compared to usual care alone.2,3 To expand on this, the researchers wanted to determine the long-term effectiveness of the intervention, specifically about disease activity after 2 years.
After the initial 16-week randomised period, the control group also received the intervention, and participants were followed for 2 years with biannual visits and six adherence-promoting webinars annually. People with DAS28 <2.6 also received a protocol as a suggested approach to tapering their antirheumatic medication – under the supervision of their rheumatologist – and any treatment changes were recorded.
62% of the original trial completers also completed the 2-year follow-up. Those who discontinued most often indicated that this was because they were too busy, unreachable, or did not permit for the second year of the extension study.
The long-term results showed that improvement in DAS28 was maintained for 2 years after completing the intervention – and was significantly lower compared to baseline. Tender joint count and general health components of the DAS28 also improved significantly, although there was no significant difference in the erythrocyte sedimentation rate and swollen joint count compared to baseline. Results were similar in people who completed the 2-year extension study versus those who discontinued prematurely.
Of the 39 participants who completed their follow-up and used disease-modifying antirheumatic medication, 44% could decrease or stop, 26% had stable usage, and 31% had increased medication. Of those with stable or decreased medication compared to baseline, 65% had improved DAS28.
After the 2-year follow-up, HDL cholesterol increased, and C-reactive protein (CRP) remained significantly lower compared to baseline values—although there was no longer a significant difference in weight, waist circumference, LDL cholesterol, or HbA1c.
These findings indicate that intensive lifestyle modifications can be effective in the long term for people with RA.
New work on the precursors of inflammatory synovial macrophages
Rheumatoid arthritis (RA) is an autoimmune disease that causes joint inflammation and destruction.1 There is currently no cure – and although there are many treatments, their effectiveness varies from person to person, suggesting an undefined pathogenic diversity.1 Deep characterisation of myeloid cell subsets by single-cell RNA sequencing across healthy and inflamed tissues in RA has identified new pathogenic cell states and subsets – with data from five large-scale studies. However, subset overlap across studies and compartments – such as in blood versus synovial tissue – has not yet been systematically investigated.
Presenting at the 2024 EULAR congress in Vienna, Sebastien Viatte explained, “We wanted to map monocyte subsets and states across studies and compartments to identify blood monocyte precursors of inflammatory synovial macrophage subsets observed in people with RA.”
With this in mind, the group set out to discover whether quiescent human blood monocyte states are pre-committed to an inflammatory synovial transcriptional program. First, peripheral blood mononuclear cells (PBMC) from healthy volunteers and RA patients with clinically well-controlled disease (quiescent PBMC) were enriched for monocytes by negative selection and subjected to single-cell RNA sequencing. The researcher then used published myeloid cell subsets to map onto their template based on the similarity of their expression scores. Hierarchical methods were applied to merge similar clusters and create a consensus map, and random forests were used to merge over-clustered data and identify novel myeloid cell states – and generate a final taxonomy of monocyte states in healthy human blood. Finally, to provide experimental validation at the protein level, PBMC from 19 RA patients with uncontrolled inflammation were deeply immunophenotyped, and inflammatory cell states with increased abundance in RA were identified.
All told, this work generated an exhaustive reference atlas comprising 11 monocyte states across anatomical compartments relevant to RA. For example, it was possible to show that different clusters, in fact, represent the same inflammatory synovial macrophage subset and are transcriptionally similar to an IL1B+ monocyte subset present in quiescent peripheral blood.
The findings also revealed that four quiescent monocyte states in the peripheral blood of both RA patients and healthy individuals expand in the blood of patients with uncontrolled RA. These likely represent blood precursors of pathogenic tissue macrophages.
This work is important because it not only defines a new monocyte cell taxonomy relevant for RA – with 11 continuous cell states that dynamically transition into each other across anatomical compartments – but also identifies potential blood precursors of pathogenic tissue macrophages.
The 2024 EULAR congress in Vienna included a clinical abstract session focusing on pain and prognosis in RA. Two groups presented their research into ways to characterise early RA.
The first looked at dissecting early RA patient trajectories through time-independent disease state patterns of inflammation in blood or joints. Presenting the work, Nils Steinz said, “Previous studies have identified smooth time trajectories of rapid, slow, or no progression of disease activity, assessed through DAS28. In real life, we observe more chaotic disease evolvements – and particularly the detours could indicate a lack of adequate treatment.”
To address this, the researchers set out to discern trajectories of disease states of early RA over 1.5 years from the first outpatient clinic visit in real-world data from 1,237 patients with 5,017 visits to the Leidenatology outpatient clinic. This was achieved using pseudo-time graph-based analyses of clustered visit data. Subsequently, patient trajectories were identified using a matching algorithm. The pipeline was applied to data from the TACERA cohort to validate the findings, representing 244 patients with early seropositive RA. The results showed eight disease states, with swollen and tender joint count, erythrocyte sedimentation rate (ESR), and leukocytes as the major discriminating factors. One cluster was the clinically optimal disease state with the least inflammation overall. The sequence of disease states experienced by patients was grouped together to form four distinct trajectories. Some people had high ESR clusters at the start and end. Others showed rapid progression towards the ideal cluster with no disease activity. The third group transitioned through a high leucocyte state, and the fourth was classed as having a poor prognosis. In the TACERA analysis, the team found similar clusters and trajectories. These revealed interesting differences in age, gender, and serostatus – even though these variables were not included in the clustering. The differences were not driven by inclusion date, follow-up duration, symptom duration, or time to methotrexate initiation. However, except for the people with high ESR clusters at start and end, baseline variables could not predict the trajectories.
These trajectories draw a more granular picture than previously described and show early RA patients stranded in suboptimal disease states, with inflammation and poor response as the main discriminatory factors. This approach provides insights into opportunities for improving care, such as more intensive treatment at an earlier stage. Work is ongoing, and the next step will be to characterise the immune profiles of the different disease states and discern the impact of treatment decisions.
The second work also looked at patterns but with a focus on non-articular pain – an issue which is common in early RA and associated with lower remission rates.2 “Understanding common phenotypes of regional non-articular pain and the role of RA-related joint inflammation could help better personalise RA care,” said Charis Meng – presenter of the work from the Canadian Early Arthritis Cohort Study.
Data from 392 early RA patients were used to describe common non-articular pain presentations around the time of early RA diagnosis, as well as evolution over the first year of treatment, and associations with active inflammation. Prespecified pain patterns were classified based on non-articular pain reported in the four body quadrants and axial region,2,3 excluding hands and feet, and grouped as: no pain, regional, or widespread. Descriptive statistics were used to summarise the frequency and evolution of different patterns at baseline and over 12-month follow-up.
Over half of patients reported prevalent non-articular pain at baseline, of which nearly three-quarters were classed as regional. The most frequent patterns were axial (34%), pain in both upper quadrants (20%), and both lower quadrants (10%). Of those with prevalent regional pain, it persisted or worsened in 42% over 1 year. But for those with prevalent widespread pain, 73% resolved or improved to a regional pattern. Joint inflammation tended to be more frequently reported in corresponding locations with non-articular pain, and often persisted over follow-up. These findings suggest that non-articular pain is common in early RA and throughout the first year after diagnosis. The significantly higher frequency of tender or swollen joints within most areas of non-articular pain over time suggests RA activity may be a contributing factor. More studies are needed, but early intervention to prevent and treat non-articular pain in early RA is recommended.
The synovial tissue inflammation seen in RA shows a high degree of heterogeneity – which may be a factor in people’s variable response to treatments. We also know that distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis. The potential of high-throughput analyses has been shown, and these technologies can help dissect disease heterogeneity and identify novel biomarkers that could be used in prognosis.
To explore this further, 373 treatment-naïve RA patients were enrolled and given an ultrasound-guided synovial tissue biopsy. The synovitis degree and synovial pathotype were then determined for each individual. A subset of 45 samples was used for synovial tissue macrophage phenotyping and profiling to measure the abundance of distinct macrophage populations. Moreover, the transcriptomic profile of CD68pos cells in distinct regions of interest within the synovial tissue was determined using spatial technology. After study entry, patients were managed with a treat-to-target strategy.
The findings showed that those patients who reached disease remission at 6 months had a lower Krenn Synovitis Score (KSS) at baseline compared to people who did not achieve this outcome. People who had been stratified based on synovial pathotype as lympho-myeloid or diffuse-myeloid pathotype had a lower response to conventional synthetic disease-modifying antirheumatic drugs (csDMARD) compared to people with a pauci-immune pathotype. However, further analysis suggested that, at an individual level, baseline KSS has limited capacity to distinguish between responders and non-responders, which highlights the need for multi-modal tissue deconvolution.
Flow cytometry analysis revealed that those with lympho-myeloid or diffuse-myeloid pathotypes showed comparable enrichment of two distinct synovial tissue macrophage populations (MerTKposCD206pos and MerTKnegCD206neg), while patients with the pauci-immune pathotype showed a predominance of MerTKposCD206pos. The enrichment of MerTKposCD206pos synovial tissue macrophages was also higher in people who achieved remission at 6 months. Notably, enrichment of these MerTKpos synovial tissue macrophages greater than 44.3% from baseline was shown to be an independent factor associated with achieving remission at 6 months.
Digital spatial profiling of synovial tissue biopsies revealed differential gene networks activating the macrophages in distinct tissue locations. This method was also able to identify transcriptomic signatures of synovial tissue macrophages in the lining and sublining location that were associated with response to csDMARD. Integration of sequencing and transcriptomic data resulted in the group being able to map synovial tissue macrophages clusters and stratify them based on treatment response.
Such multi-modal analysis of synovitis could enable differentiation of treatment-naïve RA patients at their first medical evaluation, and the data strongly support the predictive value as a patient-based decision test tool.
Differences in socioeconomic status (SES) are known to be linked to differences in the risk of developing disease. While people with lower SES are more likely to develop complex diseases such as diabetes and cardiovascular disease, those with a higher SES are at increased risk of developing certain types of cancer. Using biobank and national register data, researchers from Finland have now found that people with lower SES (educational achievement and occupation) have a greater genetic susceptibility to develop many other complex diseases such as rheumatoid arthritis, lung cancer, depression, and alcohol use disorder, as well as Type 2 diabetes, whereas those with a higher SES are more at risk of developing breast, prostate, and all cancers.
Dr Fiona Hagenbeek, a postdoctoral researcher at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland, who will present her group‘s work at the annual conference of the European Society of Human Genetics today (Sunday), says that these promising initial results mean that it is likely that polygenic risk scores, which measure an individual‘s risk of a particular disease based on genetic information, could be added to the screening protocols for multiple diseases and in several countries. “Understanding that the impact of polygenic scores on disease risk is context-dependent may lead to further stratified screening protocols,“ she says. “For example, in the future, screening protocols for breast cancer may be adapted so that females with a high genetic risk and who are highly educated receive earlier or more frequent screening than females with lower genetic risk or less education.”
The researchers used genomics, SES, and health data from approximately 280,000 Finnish individuals in the FinnGen study, a research project in genomics and personalised medicine that aims to understand the genetic basis of diseases. The participants were aged 35 – 80 at the time of entry into the study. The study aimed to systematically assess the evidence of gene-environment interaction (GxE) through the differing genetic susceptibility to disease in diverse socioeconomic groups. While previous studies have shown the presence of such a difference in risk, this is the first to systematically assess GxE for SES in 19 complex diseases that have a high burden in high-income countries.
“Most clinical risk prediction models include basic demographic information such as biological sex and age, recognising that disease incidence differs between males and females, and is age-dependent,“ says Dr Hagenbeek. “Acknowledging that such context also matters when incorporating genetic information into healthcare is an important first step. But now, we can show that the genetic prediction of disease risk also depends on an individual’s socioeconomic background. So while our genetic information does not change throughout our lifetime, the impact of genetics on disease risk changes as we age or change our circumstances.“
The researchers hope that the study will be followed up to see whether further differences can be identified when looking at more specific aspects of educational and professional achievement. While their current results for profession generally mirror those for education, they do not match completely, indicating that each can provide unique information on the interplay of socioeconomic status and genetics on disease risk. Expanding the list of socioeconomic indices to be studied may bring about new insights into how the overlapping aspects of a person’s socioeconomic environment may, together with genetic information, influence their disease risk.
They will also compare their results across biobank studies from Finland, UK, Norway, and Estonia through the INTERVENE* consortium, allowing them to determine whether there are country or biobank-specific issues involved. “Our study focused solely on individuals of European ancestry, and it will also be important in the future to see whether our observations concerning the interplay of socioeconomic status and genetics for disease risk are replicated in people of multiple ancestries in higher and lower-income countries,“ says Dr Hagenbeek. “As the overall aim of incorporating genetic information into healthcare is to facilitate personalised medicine, we should not treat genetic information as ‘one size fits all‘. Rather, we should investigate and then include the circumstances that modify genetic risk when carrying out disease prediction.“
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