Background

The evaluation of Patient Reported Outcomes Measurement Information System (PROMIS) computerized adaptive test (CAT) in adults with systemic lupus erythematous (SLE) is an emerging field of research. We aimed to examine the test-retest reliability and construct validity of the PROMIS CAT in a Canadian cohort of patients with SLE.

Methods

Two hundred twenty-seven patients completed 14 domains of PROMIS CAT and seven legacy instruments during their clinical visits. Test-retest reliability of PROMIS was evaluated 7-10 days from baseline using intraclass correlation coefficient (ICC (2; 1)). The construct validity of the PROMIS CAT domains was evaluated against the commonly used legacy instruments, and also in comparison to disease activity and disease damage using Spearman correlations. A multitrait-multimethod matrix (MMM) approach was used to further assess construct validity comparing selected 10 domains of PROMIS and SF-36 domains.

Results

Moderate to excellent reliability was found for all domains (ICC [2;1] ranging from lowest, 0.66 for Sleep Disturbance and highest, 0.93 for the Mobility domain). Comparing seven legacy instruments with 14 domains of PROMIS CAT, moderate to strong correlations (0.51-0.91) were identified. The average time to complete all PROMIS CAT domains was 11.7 min. The MMM further established construct validity by showing moderate to strong correlations (0.55-0.87) between select PROMIS and SF-36 domains; the average correlations from similar traits (convergent validity) were significantly greater than the average correlations from different traits.

Conclusions

These results provide evidence on the reliability and validity of PROMIS CAT in SLE in a Canadian cohort.

Background

Management of lupus nephritis (LN) would be greatly aided by the discovery of biomarkers that accurately reflect changes in disease activity. Here, we used a proteomics approach to identify potential urinary biomarkers associated with LN.

Methods

Urine was obtained from 60 LN patients with paired renal biopsies, 25 active non-LN SLE patients, and 24 healthy controls. Using Luminex, 128 analytes were quantified and normalized to urinary creatinine levels. Data were analyzed by linear modeling and non-parametric statistics, with corrections for multiple comparisons. A second cohort of 33 active LN, 16 active non-LN, and 30 remission LN SLE patients was used to validate the results.

Results

Forty-four analytes were identified that were significantly increased in active LN as compared to active non-LN. This included a number of unique proteins (e.g., TIMP-1, PAI-1, PF4, vWF, and IL-15) as well as known candidate LN biomarkers (e.g., adiponectin, sVCAM-1, and IL-6), that differed markedly (>4-fold) between active LN and non-LN, all of which were confirmed in the validation cohort and normalized in remission LN patients. These proteins demonstrated an enhanced ability to discriminate between active LN and non-LN patients over several previously reported biomarkers. Ten proteins were found to significantly correlate with the activity score on renal biopsy, eight of which strongly discriminated between active proliferative and non-proliferative/chronic renal lesions.

Conclusions

A number of promising urinary biomarkers that correlate with the presence of active renal disease and/or renal biopsy changes were identified and appear to outperform many of the existing proposed biomarkers.

Objective

We investigated the autoantibody (autoAb) profiles in ANA+ individuals lacking systemic autoimmune rheumatic disease (SARD) and early SARD patients to determine the key differences between these groups and identify factors that are associated with an increased risk of symptomatic progression within the next 2 years in ANA+ individuals.

Methods

Using custom antigen (Ag) microarrays, 144 IgM and IgG autoAbs were surveyed in 84 asymptomatic and 123 symptomatic (48 UCTD and 75 SARD patients) ANA+ individuals. AutoAbs were compared in ANA+ individuals lacking a SARD diagnosis with ≥2 years follow-up (n = 52), including all those who demonstrated progression (n = 14) during this period, with changes over time assessed in a representative subset.

Results

We show that ANA+ individuals have autoAb to many self-Ags that are not being captured by current screening techniques and very high levels of these autoAbs are predominantly restricted to early SARD patients, with SLE patients displaying reactivity to many more autoAgs than the other groups. In general, the symptoms that developed in progressors mirrored those seen in SARD patients with similar patterns of autoAbs. Only anti-Ro52 Abs were found to predict progression (positive predictive value 46%, negative predictive value 89%). Surprisingly, over 2 years of follow-up the levels of autoAbs remained remarkably stable regardless of whether individuals progressed or not.

Conclusion

Our findings strongly argue that development of assays with an expanded set of auto-Ags and enhanced dynamic range would improve the diagnostic and prognostic ability of autoAb testing.

Both a lack of biomarkers and relatively ineffective treatments constitute impediments to management of lupus nephritis (LN). Here we used gene expression microarrays to contrast the transcriptomic profiles of active SLE patients with and without LN to identify potential biomarkers for this condition. RNA isolated from whole peripheral blood of active SLE patients was used for transcriptomic profiling and the data analyzed by linear modeling, with corrections for multiple testing. Results were validated in a second cohort of SLE patients, using NanoString technology. The majority of genes demonstrating altered transcript abundance between patients with and without LN were neutrophil-related. Findings in the validation cohort confirmed this observation and showed that levels of RNA abundance in renal remission were similar to active patients without LN. In secondary analyses, RNA abundance correlated with disease activity, hematuria and proteinuria, but not renal biopsy changes. As abundance levels of the individual transcripts correlated strongly with each other, a composite neutrophil score was generated by summing all levels before examining additional correlations. There was a modest correlation between the neutrophil score and the blood neutrophil count, which was largely driven by the dose of glucocorticosteroids and not the proportion of low density and/or activated neutrophils. Analysis of longitudinal data revealed no correlation between baseline neutrophil score or changes over the first year of follow-up with subsequent renal flare or treatment outcomes, respectively. The findings argue that although the neutrophil score is associated with LN, its clinical utility as a biomarker may be limited.

Objective

We previously demonstrated the utility of the Automated Neuropsychological Assessment Metrics (ANAM) for screening cognitive impairment (CI) in patients with systemic lupus erythematosus (SLE) and developed composite indices for interpreting ANAM results. Our objectives here were to provide further support for the ANAM's concurrent criterion validity against the American College of Rheumatology neuropsychological battery (ACR-NB), identify the most discriminatory subtests and scores of the ANAM for predicting CI, and provide a new approach to interpret ANAM results using Classification and Regression Tree (CART) analysis.

Methods

300 adult SLE patients completed an adapted ACR-NB and ANAM on the same day. As per objectives, six models were built using combinations of ANAM subtests and scores and submitted to CART analysis. Area under the curve (AUC) was calculated to evaluate the ANAM's criterion validity compared to the adapted ACR-NB; the most discriminatory ANAM subtests and scores in each model were selected, and performance of models with the highest AUCs were compared to our previous composite indices; decision trees were generated for models with the highest AUCs.

Results

Two models had excellent AUCs of 86 and 89%. Eight most discriminatory ANAM subtests and scores were identified. Both models demonstrated higher AUCs against our previous composite indices. An adapted decision tree was created to simplify the interpretation of ANAM results.

Conclusion

We provide further validity evidence for the ANAM as a valid CI screening tool in SLE. The decision tree improves interpretation of ANAM results, enhancing clinical utility.

OBJECTIVE: To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. METHODS: SLE patients ages 18-65 years underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data were collected on demographic and clinical variables, disease burden/activity, health-related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal-Wallis and χ2 tests. RESULTS: Of the 238 patients, 90% were female, with a mean age of 41 years (s.d. 12) and a disease duration of 14 years (s.d. 10) at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (P < 0.03) compared with subtype B. Subtype A also had greater levels of subjective cognitive function (P < 0.001), disease burden/damage (P < 0.04), HRQoL (P < 0.001) and psychiatric measures (P < 0.001) compared with subtype B. CONCLUSION: This study demonstrates the complexity of cognitive impairment (CI) in SLE and that individual, multifactorial phenotypes exist. Those with greater disease burden, from SLE-specific factors or other factors associated with chronic conditions, report poorer cognitive functioning and perform worse on objective cognitive measures. By exploring different ways of phenotyping SLE we may better define CI in SLE. Ultimately this will aid our understanding of personalized CI trajectories and identification of appropriate treatments.