Abstract
Objective: Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. SLE is characterized by high inter-patient variability, including fluctuations over time, a factor which most biomarker studies omit from consideration. We investigated relationships between disease activity and biomarker expression in SLE, using novel methods to control for time-dependent variability, in a proof-of-concept study to evaluate whether doing so revealed additional information.Methods: We measured 4 serum biomarkers (MIF, CCL2, CCL19, and CXCL10) and 13 routine clinical laboratory parameters, alongside disease activity measured by the SLE disease activity index-2k (SLEDAI-2k), collected longitudinally. We analyzed these data with unsupervised learning methods via ensemble clustering, incorporating temporal relationships using dynamic time warping for distance metric calculation.Results: Data from 843 visits in 110 patients (median age 47, 83% female) demonstrated highly heterogeneous time-dependent relationships between disease activity and biomarkers. Unbiased magnitude-based hierarchical clustering of biomarker expression levels isolated a patient subset (n = 9) with distinctively heterogeneous expression of the 17 biological parameters, and who had MIF, CCL2, CCL19, and CXCL10 levels that were higher and more strongly associated with disease activity, based on leave-one-out cross-validated regression analysis. In the remaining subgroup, a time-dependent regression model revealed significantly stronger predictive power of biomarkers for disease activity, compared to a time-agnostic regression model. Despite no significant difference in simple magnitude, using dynamic time warping analysis to align longitudinal profiles revealed a large subset (n = 69) with significantly stronger associations between biological parameters and disease activity. This subgroup had significantly lower flare rates, disease activity and damage scores, suggesting this clustering is clinically meaningful.Conclusions: These results suggest associations between biological parameters and disease activity in SLE exist in a multi-dimensional time-dependent pattern, with implications for the analysis of biomarkers in SLE often used to identify therapeutic targets. Novel methods to analyse high-dimensional data and control for time-dependent variability may have broad utility in the study complex relationships between clinical and biological parameters.
Highlights
Systemic lupus erythematosus (SLE) is an archetypal multisystem autoimmune disease, which results in a marked loss of life expectancy, a fact that has changed little in recent decades as “breakthrough” treatments have not emerged [1, 2]
Data were obtained for the period May 2007 to December 2012 from the Australian Lupus Registry [15], where SLE patients over 18 years old fulfilling the 1982 American College of Rheumatology (ACR) revised criteria [16] have been recruited and longitudinal clinical data and serum samples archived, as previously described [4, 5, 17]
Amongst the data collected at each visit, the present study investigated all 13 routinely collected clinical laboratory parameters [C-reactive protein (CRP), complement components component 3 (C3) and component 4 (C4), hemoglobin (Hb), total white cell count (WCC), platelets, neutrophils, lymphocytes, erythrocyte sedimentation rate (ESR), anti-double-stranded DNA antibodies, and urine protein/creatinine ratio (UPCR), urine WCC, and urine red cell count]
Summary
Systemic lupus erythematosus (SLE) is an archetypal multisystem autoimmune disease, which results in a marked loss of life expectancy, a fact that has changed little in recent decades as “breakthrough” treatments have not emerged [1, 2]. Patients diagnosed with SLE share autoimmunity to nucleic acids and immune-mediated tissue damage, SLE is characterized by high inter-patient variability in terms of clinical and biological characteristics, suggesting value in identifying biologicallydefined subsets of patients for the application of targeted therapies [3] Studies of biomarkers such as serum cytokines have been used to in these attempts, but to date robust relationships between such analytes and disease activity measures have been elusive [4, 5]. The marked time-dependent volatility of SLE, and the likely existence of distinct subsets within the disease, have typically not been addressed in biomarker studies; associations that have been identified between biomarkers and disease activity are modest at best, suggesting that dynamic relationships between biological data and disease activity may be missed by traditional analytical approaches and clouded by the pooling of heterogeneous patients. This is especially true of cross-sectional data, and multiple studies have underscored the need to study associations in longitudinal fashion [7, 8], allowing consideration of the temporal or time dimension
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