ArticleFrontiers in immunology2024
Prediction of treatment response in lupus nephritis using density of tubulointerstitial macrophage infiltration.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Forgotten compartment: Impact of tubulointerstitial inflammation and damage on renal outcomes in lupus nephritis from Saudi Arabia.World journal of experimental medicine · 2026Article
- Capturing the inflammatory landscape within kidney compartments of human diabetic kidney disease: a digital spatial profiling study.Frontiers in endocrinology · 2026Article
- Dynamic macrophage phenotypes in autoimmune and inflammatory rheumatic diseases.Nature reviews. Rheumatology · 2025Review
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Authors and funding
10 authors at 3 institutions in 1 country.
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Abstract
Background: Lupus nephritis (LN) is a common disease with diverse clinical and pathological manifestations. A major challenge in the management of LN is the inability to predict its treatment response at an early stage. The objective of this study was to determine whether the density of tubulointerstitial macrophage infiltration can be used to predict treatment response in LN and whether its addition to clinicopathological data at the time of biopsy would improve risk prediction. Methods: In this retrospective cohort study, 430 patients with LN in our hospital from January 2010 to December 2017 were included. We used immunohistochemistry to show macrophage and lymphocyte infiltration in their biopsy specimens, followed by quantification of the infiltration density. The outcome was the treatment response, defined as complete or partial remission at 12 months of immunosuppression. Results: The infiltration of CD68 Conclusion: The density of tubulointerstitial macrophage infiltration is an independent predictor for treatment response in LN. Adding tubulointerstitial macrophage infiltration density to clinicopathological data at the time of biopsy significantly improves risk prediction of treatment response in LN patients.
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