Arthritis Research Canada at
Lupus 2025
May 23
International effort in harmonising cognitive impairment research in systemic lupus erythematosus. #O043
An international group of experts came together to discuss how cognitive problems in lupus are studied. They agreed that more consistent tools and methods, especially in testing and brain imaging, are needed to better understand and compare results across studies. This initiative may help pave the way for more consistent and collaborative studies on lupus, which can lead to better diagnosis, treatment, and support for patients.
Research Team: Barraclough M, Mckie S, Rittner L, Knight A, Katz P, Hanly K, Legge L, Mackay M, Dobrowolski C, Brunner H, Ogbu E, Wincup C, Govoni M, Bortoluzzi A, Silvagni E, Ruttan L, Bingham E, Green R, TartagliaC, Lee S, Fisk J, Uludag K, Tang C, Valdés Cabrera D, Anderson E, Difrancesco M, Hoi A, Raghunath S, Kozora E, He Z, Elliott R, Parker B, Bruce I, Touma Z, Appenzeller S.
Direct and indirect costs associated with damage accrual: results from the systemic lupus international collaborating clinics (SLICC) inception cohort. #O030
This international study of people with lupus (the SLICC cohort) found that people face high costs not only from medical care, but also from lost productivity at work and in their daily activities, especially if they have more severe disease. This shows us that the biggest costs of lupus are often the time and work people lose, rather than their medical bills. This is important for understanding the fulsome impact of lupus.
Research Team: Barber M, Hanly J, Urowitz M, Bruce I, St. Pierre Y, Gordon C, Bae SC, Romero-Diaz J, Sanchez-Guerrero J, Bernatsky S, Wallace DJ, Isenberg D, Rahman A, Merrill J, Fortin PR, Gladman DD, Petri M, Ginzler E, Dooley MA, Ramsey-Goldman R, Manzi S, Jönsen A, Alarcón GS, Van Vollenhoven R, Aranow C, Mackay M, Ruiz-Irastorza G, Lim SS, Inanc M, Kalunian K, Jacobsen S, Peschken C, Kamen DK, Askanase A, Clarke AE.
Machine learning can identify an antinuclear antibody pattern that may rule out systemic autoimmune rheumatic diseases. #O032
The researchers used artificial intelligence to create a tool that can accurately tell apart patterns of a laboratory test (ANA test) that indicate a person likely does not have an autoimmune rheumatic disease, from other patterns that suggest they might have an autoimmune disease. This tool could help avoid unnecessary tests and doctor’s visits, and allow patients to get the care they need faster.
Research Team: Moghaddam F, Sajadi J, Clarke A, Bernatsky S, Costenbader K, Urowitz M, Hanly J, Gordon C, Bae SC, Romero-Diaz J, Sanchez-Guerrero J, Wallace DJ, Isenberg D, Rahman A, Merrill J, Fortin PR, Gladman DD, Bruce I, Petri M, Ginzler E, Dooley MA, RamseyGoldman R, Manzi S, Jönsen A, Alarcón GS, Van Vollenhoven R, Aranow C, Mackay M, Ruiz-Irastorza G, Lim S, Inanc M, Kalunian K, Jacobsen S, Peschken C, KamenDL, Askanase A, Fritzler M, Aminghafari M, Choi M.
Serum S100A8/A9,MMP-9 and IL-6 are associated with impairment in executive function, simple attention and processing speed in patients with systemic lupus erythematosus. #O045
This international study of people with lupus (SLICC cohort) found that certain blood markers are tied to cognitive problems like thinking and memory. This may help explain cognitive issues experienced by people with lupus and allow earlier detection, or lead to the development of new treatments for these challenges.
Research Team: Neary E, Munoz-Grajales C, Withers J, Diaz Martinez JP, Barraclough M, Bingham K, Kretzmann R, Tartaglia C, Ruttan L, Choi M, Appenzeller S, Marzouk S, Bonilla D, Katz P, Beaton D, Green R, Whittall-Garcia L, Gladman D, Touma Z.
Contemporary kidney outcomes among patients with lupus nephritis in the United States. #O036
This study found that about one-third of lupus patients with kidney involvement (known as lupus nephritis) in the United States developed serious kidney failure, and this was more common among Black and male patients. This highlights the need to improve early treatment and access to kidney care, especially for Black and male lupus patients.
Research Team: Patel A, Zhang L, Zhou B, Choi H, Jorge A
Rheumatology diagnostics utilizing artificial intelligence for ANA pattern identification and titre quantification. #PT012
The authors used artificial intelligence to develop a machine learning tool that can accurately interpret lab tests used to help diagnose autoimmune rheumatic diseases like lupus. This is important because a tool like this can reduce the chance of human error and also help speed up the testing and diagnosis processes, so people get an accurate diagnosis, and the care they need., more quickly and consistently.
Research Team: Moghaddam F, Sajadi J, Clarke AE, Bernatsky S, Costenbader K, Chen I, Urowitz M, Hanly J, Gordon C, Bae SC, Romero-Diaz J, Sanchez-Guerrero J, Wallace DJ, Isenberg D, Rahman A, JMerrill J, Fortin PR, Gladman DD, Bruce I, Petri M, Ginzler E, Dooley MA, Ramsey Goldman R, Manzi S, Jönsen A, Alarcón GS, Van Vollenhoven R, Aranow C, Mackay M, Ruiz-Irastorza G, Lim SS, Inanc M, Kalunian K, Jacobsen S, Peschken C, Kamen DL, Askanase A, Fritzler M, Aminghafari M, Choi MY.
