{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T21:04:06Z","timestamp":1777928646377,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2017,4,6]],"date-time":"2017-04-06T00:00:00Z","timestamp":1491436800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Lupus"],"published-print":{"date-parts":[[2017,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Participants from the UK JSLE Cohort Study, aged &lt;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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>At best, combining common classic blood biomarkers of lupus activity using multivariate analysis provides a \u2018fair\u2019 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0961203317702253","type":"journal-article","created":{"date-parts":[[2017,4,7]],"date-time":"2017-04-07T02:03:29Z","timestamp":1491530609000},"page":"1212-1217","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Do classic blood biomarkers of JSLE identify active lupus nephritis? Evidence from the UK JSLE Cohort Study"],"prefix":"10.1177","volume":"26","author":[{"given":"E M D","family":"Smith","sequence":"first","affiliation":[{"name":"Department of Women\u2019s and Children\u2019s Health, University of Liverpool, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A L","family":"Jorgensen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Liverpool, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M W","family":"Beresford","sequence":"additional","affiliation":[{"name":"Department of Women\u2019s and Children\u2019s Health, University of Liverpool, UK"},{"name":"Department of Paediatric Rheumatology, Alder Hey Children\u2019s NHS Foundation Trust, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,4,6]]},"reference":[{"key":"bibr1-0961203317702253","doi-asserted-by":"publisher","DOI":"10.1016\/j.rdc.2009.12.012"},{"key":"bibr2-0961203317702253","doi-asserted-by":"publisher","DOI":"10.1002\/art.34410"},{"key":"bibr3-0961203317702253","doi-asserted-by":"publisher","DOI":"10.3899\/jrheum.130005"},{"key":"bibr4-0961203317702253","doi-asserted-by":"publisher","DOI":"10.1177\/0961203315570688"},{"key":"bibr5-0961203317702253","doi-asserted-by":"publisher","DOI":"10.1093\/rheumatology\/keu343"},{"key":"bibr6-0961203317702253","doi-asserted-by":"publisher","DOI":"10.1007\/s00296-011-1940-9"},{"key":"bibr7-0961203317702253","doi-asserted-by":"publisher","DOI":"10.1093\/rheumatology\/keh624"},{"key":"bibr8-0961203317702253","unstructured":"R Core Team.\n                      R: A language and environment for statistical computing R Foundation for Statistical Computing\n                      . 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