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Is there ‘bang for the buck’ using big data to help predict who will develop RA in at-risk populations and similarly to predict response to csDMARDs, TNFi and JAKi in RA?
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Artificial intelligence isn’t a distant frontier anymore. It’s here, and it appears able to detect signs of rheumatoid arthritis, possibly before we can clinically detect it. Two studies presented at this year’s ACR meeting highlight just how close we may be to a future where algorithms flag disease before we can and monitor activity with minimal patient burden.
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We know that pre-RA is a definition that stands under a large umbrella, ranging from asymptomatic individuals with ACPA positivity to individuals with symptomatic pre-clinical synovitis. How can we predict the transition from the at-risk state to clinical RA outside the realm of clinical, conventional serological (RF/anti-CCP) or imaging (US/MRI synovitis) biomarkers?
Jiha Lee
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Real-world JAK persistence in RA is low: <30% at 2 years across Medicare & Marketscan data (n≈20K). Baricitinib lowest persistence; TOF & UPA slightly higher. Oral convenience hasn’t solved long-term adherence. Why? Safety, cost, access?
@RheumNow #ACR25 A#0369
Jiha Lee
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Only 32% of RA pts were satisfied with current txs. In a discrete choice experiment of 354 US pts, many preferred a vagus nerve stimulator over another biologic/JAKi, especially those with prior tx failures. Cost, function, and fatigue drove preferences.
@RheumNow #ACR25 A#0370
Jiha Lee
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Can the blood reflect what’s happening in the joint? In early RA, 11 synovial proteins mirrored serum levels, forming an inflammatory signature linked to disease activity and csDMARD response. High-signature pts had stronger treatment responses (AUC>0.8)
@RheumNow #ACR25 A#0070
David Liew
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Fascinating: tDR-1, a microbial sRNA, appears protective for RA in at risk ACPA+ pts:
- tDR-1 higher = less develop RA
- can predict seropos RA development
- in vitro type 1 IFN mapping checks out
Useful in itself, but importantly: why does it happen?
#ACR25 ABST0775 @RheumNow https://t.co/ep99dNAzFV
Richard Conway
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Novelli et al. Upadacitinib in male vs female PsA. Data from SELECT-PsA studies. Appears equivalent in males and females, contrasting with TNFi/IL17i data. @RheumNow #ACR25 Abstr#557 https://t.co/fMKsbCkju0
Md Yuzaiful Md Yusof
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#ACR25 Abstr#775 Previous study showed plasma tRNA-derived RNA (tDR-1), produced by bacteria from the phylum Proteobacteria, was assoc. with lower RA activity.
In 60 At-Risk CCP3+ people, Plasma tDR-1 improved prediction of RA. Need validation & adoption in practice @RheumNow https://t.co/XBmtohBEHc
Mrinalini Dey
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#0191 We found that in a large South London RA cohort, use of advanced therapy in moderate disease (DAS28 3.2-5.1) generally followed NICE guidance. Gaps often reflected telehealth limiting DAS assessment and patient hesitancy. Self-assessment tools may help. @RheumNow #ACR25 https://t.co/UMGtkfABHn
Richard Conway
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Albach et al. CAR T-cells in D2T RA. 6 patients. Safety looks ok. ACR20 in 4/6, ACR50 in 2/6. ACPA and RF disappeared. Results maybe not as positive as we might have anticipated? @RheumNow #ACR25 Abstr#471 https://t.co/TaEVWiWsNm
Aurelie Najm
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tRNA-derived RNA (tDR-1), circulating microbial RNA
Associated w/ reduction in risk of RA conversion in asymptomatic CCP+ individuals (60 vs. 120+ ctrls),
‼️ tDR-1 alone over performs prediction model w/ smoking shared episode and RF w/ AUC 0.86
What is the mechanism? tDR-1 https://t.co/fuyk4NeOyA
Richard Conway
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Ribeiro et al. Case series of combination bDMARD/tsDMARD in PsA from Toronto. 24 patients, median just over a year follow-up. Appear effective, especially for objective skin and joint measures. No major safety concerns but short follow-up to date. @RheumNow #ACR25 Abstr#565 https://t.co/Zk3odT2EX2
Mrinalini Dey
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#0775
Higher circulating microbial small RNA tDR-1 linked to lower risk of developing RA in CCP3+ individuals (AUC 0.86).
tDR-1 downregulates interferon-response genes in vitro, suggesting microbiome-mediated anti-inflammatory mechanism in RA pathogenesis. @RheumNow #ACR25
Akhil Sood MD, MS
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SLE & arthritis: Is there a difference?
Abstract #0629:
- Non-deforming (ND): 73% | Jaccoud’s: 19% | Rhupus: 8%
- Vs ND, Rhupus had lower odds of type I IFN signature
- Time to deformities: Jaccoud's ~2 yrs vs Rhupus ~5 yrs
- RNP+ predictive of Jaccoud’s
@RheumNow #ACR25
Jiha Lee
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Why do some clinics excel at RA disease activity tracking while others struggle? In the VA, success came down to structure: RA-specific clinics, templates, and staff. Barriers? Time, missing labs, and workflow gaps.
@RheumNow #ACR25 Abstract#0222
Aurelie Najm
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What can we learn from longitudinal peripheral blood multi-omic profiling in at-risk individuals?
50 RA converters vs. non converters from StopRA study
Decision tree showing highest risk for conversion to clinical RA in Individuals w/
-CCP3 ≥108 units
-T peripheral helpers https://t.co/jw53EB6tI6
Mrinalini Dey
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#0774
Multi-omic profiling of anti-CCP3+ “at-risk” individuals reveals Tph & cytotoxic CD8+ expansions, altered gene/chromatin profiles and predictive model (AUC 0.77) for RA conversion
PTPN22 locus accessibility & Tph ≥ 2.45% identify highest risk. @RheumNow #ACR25 https://t.co/JDSMkdmoGv
Aurelie Najm
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What's new in the Pre-RA world?
Slowly unraveling mechanisms and biology associated to transition from CCP+ asymptomatic pre-RA state to clinical RA
Here is my summary of a few abstracts presented at #ACR25 proposing biomarkers beyond the realm of clinical and classical
Dr. John Cush
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6-week intensive smoking cessation prg studied in 94 RA daily smokers in remission or low Dz activity (DAS28-CRP=2.9). At 3 mos the intervention group had higher cessation rates; but had no effect on EULAR responses, TJC, or CRP (result actually favoured the controls) https://t.co/HPJYeyPCPQ
