Do classic blood biomarkers of JSLE identify active lupus nephritis? Evidence from the UK JSLE Cohort Study

• Published in: Lupus Vol. 26; no. 11; pp. 1212 - 1217
• Main Authors: Smith, E M D, Jorgensen, A L, Beresford, M W
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-10-2017; Sage Publications Ltd
• Subjects: Adolescent; Age of Onset; Anti-DNA antibodies; Antibodies; Area Under Curve; Biomarkers; Biomarkers - blood; Blood; Blood Sedimentation; Cohort analysis; Complement C3 - analysis; Complement component C3; Complement component C4; Cross-Sectional Studies; Female; Health risk assessment; Hemoglobin; Humans; Immunoglobulin G; Immunoglobulin G - blood; Kidneys; Leukocytes (neutrophilic); Logistic Models; Lupus; Lupus Erythematosus, Systemic - blood; Lupus Erythematosus, Systemic - complications; Lupus Erythematosus, Systemic - diagnosis; Lupus nephritis; Lupus Nephritis - blood; Lupus Nephritis - diagnosis; Lupus Nephritis - etiology; Lymphocyte Count; Lymphocytes; Male; Multivariate Analysis; Nephritis; Neutrophils; Predictive Value of Tests; Risk Factors; ROC Curve; Systemic lupus erythematosus; United Kingdom; Urine; C3; C4; ESR; classic biomarkers; Lupus nephritis; complement; anti-dsDNA
• ISSN: 0961-2033; 1477-0962
• DOI: 10.1177/0961203317702253

Background
Lupus nephritis (LN) affects up to 80% of juvenile-onset systemic lupus erythematosus (JSLE) patients. The value of commonly available biomarkers, such as anti-dsDNA antibodies, complement (C3/C4), ESR and full blood count parameters in the identification of active LN remains uncertain.
Methods
Participants from the UK JSLE Cohort Study, aged <16 years at diagnosis, were categorized as having active or inactive LN according to the renal domain of the British Isles Lupus Assessment Group score. Classic biomarkers: anti-dsDNA, C3, C4, ESR, CRP, haemoglobin, total white cells, neutrophils, lymphocytes, platelets and immunoglobulins were assessed for their ability to identify active LN using binary logistic regression modeling, with stepAIC function applied to select a final model. Receiver-operating curve analysis was used to assess diagnostic accuracy.
Results
A total of 370 patients were recruited; 191 (52%) had active LN and 179 (48%) had inactive LN. Binary logistic regression modeling demonstrated a combination of ESR, C3, white cell count, neutrophils, lymphocytes and IgG to be best for the identification of active LN (area under the curve 0.724).
Conclusions
At best, combining common classic blood biomarkers of lupus activity using multivariate analysis provides a ‘fair’ ability to identify active LN. Urine biomarkers were not included in these analyses. These results add to the concern that classic blood biomarkers are limited in monitoring discrete JSLE manifestations such as LN.