ArticleOncotarget2017
The prevalence, subtypes and associated factors of hyperuricemia in lupus nephritis patients at chronic kidney disease stages 1-3.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Deep learning model to predict lupus nephritis renal flare based on dynamic multivariable time-series data.BMJ open · 2024Article
- Peripheral CD4Journal of biomedical research · 2022Article
- Clinicopathological Patterns and Outcomes in Patients with Lupus Nephritis and Hyperuricemia.Journal of clinical medicine · 2022Article
- Serum Uric Acid is Associated with Renal Prognosis of Lupus Nephritis in Women but not in Men.Journal of clinical medicine · 2020Article
- Is serum uric acid a predictor of long-term renal outcome in lupus nephritis?Clinical rheumatology · 2019Article
- Polymorphism of ABCG2 Gene in Hyperuricemia Patients of Han And Uygur Ethnicity with Phlegm/Non-Phlegm Block in Xinjiang, China.Medical science monitor : international medical journal of experimental and clinical research · 2018Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a high prevalence of hyperuricemia (HUA) in the chronic kidney disease (CKD) population. However, there's a dearth of research on HUA's prevalence, subtypes, early detection, and treatment strategies of HUA in lupus nephritis (LN) patients. The aim of this study is to address these knowledge gaps. LN patients presenting to the Department of Nephrology at Shanghai Rui Jin Hospital from January 2011 to January 2016 were recruited. The effective sample size was derived using the power analysis. The demographic, clinical and laboratory characteristics of the LN patients with HUA were compared with those of patients without HUA. Two statistical models for analyzing HUA were built and compared using the receiver operating characteristic (ROC) curve analysis. The total prevalence of HUA in the cohort was 40.11%. The subtypes of HUA included urate underexcretion-type, overproduction-type and combined-type, which proportion being 67.7%, 9.7% and 22.6% respectively. The CKD stage was closely associated with the prevalence of HUA in patients with LN. The other significant associated factors were hypertension, triglycerides, serum creatinine, serum albumin, hemoglobin, parathyroid hormone, phosphorus, calcium, etc. The statistical algorithm successfully identified LN patients at risk of HUA. In conclusion, there was a high prevalence of HUA in LN patients at CKD stages 1-3, and renal underexcretion hyperuricemia was the most prevalent subtype. The occurrence of HUA in LN may be related to renal insufficiency, metabolic disorder and lupus itself. Early care coordination programs can employ risk models to improve HUA prevention and target interventions in LN patients.
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