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Record W2980544659 · doi:10.1016/j.jaut.2019.102340

Soluble urokinase plasminogen activator receptor (suPAR) levels predict damage accrual in patients with recent-onset systemic lupus erythematosus

2019· article· en· W2980544659 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Autoimmunity · 2019
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsMcGill University Health CentreUniversity of CalgaryQueen Elizabeth II Health Sciences CentreUniversité LavalUniversity of ManitobaToronto Western HospitalDalhousie UniversityUniversity of Toronto
FundersNational Center for Research ResourcesVersus ArthritisNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthNational Research Foundation of KoreaManchester Biomedical Research CentreReumatikerförbundetRegion ÖstergötlandNational Research FoundationNational Institute for Health and Care ResearchSandwell and West Birmingham Hospitals NHS TrustNational Center for Advancing Translational SciencesWellcome Trust
KeywordsSuPARMedicineUrokinasePlasminogen activatorUrokinase receptorReceptorImmunologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The soluble urokinase plasminogen activator receptor (suPAR) has potential as a prognosis and severity biomarker in several inflammatory and infectious diseases. In a previous cross-sectional study, suPAR levels were shown to reflect damage accrual in cases of systemic lupus erythematosus (SLE). Herein, we evaluated suPAR as a predictor of future organ damage in recent-onset SLE. METHODS: Included were 344 patients from the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort who met the 1997 American College of Rheumatology classification criteria with 5-years of follow-up data available. Baseline sera from patients and age- and sex-matched controls were assayed for suPAR. Organ damage was assessed annually using the SLICC/ACR damage index (SDI). RESULTS: The levels of suPAR were higher in patients who accrued damage, particularly those with SDI≥2 at 5 years (N = 32, 46.8% increase, p = 0.004), as compared to patients without damage. Logistic regression analysis revealed a significant impact of suPAR on SDI outcome (SDI≥2; OR = 1.14; 95% CI 1.03-1.26), also after adjustment for confounding factors. In an optimized logistic regression to predict damage, suPAR persisted as a predictor, together with baseline disease activity (SLEDAI-2K), age, and non-Caucasian ethnicity (model AUC = 0.77). Dissecting SDI into organ systems revealed higher suPAR levels in patients who developed musculoskeletal damage (SDI≥1; p = 0.007). CONCLUSION: Prognostic biomarkers identify patients who are at risk of acquiring early damage and therefore need careful observation and targeted treatment strategies. Overall, suPAR constitutes an interesting biomarker for patient stratification and for identifying SLE patients who are at risk of acquiring organ damage during the first 5 years of disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.270
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it