L-shaped association of systemic inflammation response index with rheumatoid arthritis in U.S. adults: Evidence from NHANES 1999 to 2020
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Updated: Jan 18, 2026

An Adoptive Transfer Model of Rheumatoid Arthritis in Mice
Published on: June 6, 2025
Min Zhao1, Ziyu Zhang2, Chunlan Pang2
1Department of Nursing, China People's Armed Police Force Tibet Autonomous Region General Hospital, Lhasa, China.
Abstract:
Rheumatoid arthritis (RA) is an autoimmune condition characterized by widespread and persistent inflammation. In this study, we sought to investigate the association between the systemic inflammation response index (SIRI) and the likelihood of RA occurrence among individuals in the United States. This study conducted a retrospective analysis using data collected from the National Health and Nutrition Examination Survey from 1999 to 2020. The relationship between SIRI and the likelihood of rheumatoid arthritis was evaluated through multivariable logistic regression models. To investigate potential nonlinear associations, restricted cubic spline functions were applied. Additionally, subgroup analyses were carried out to assess whether demographic and clinical variables modified these associations. Fourteen thousand, three hundred seventy-four participants were included in this study, among whom 1412 individuals (9.8%) were diagnosed with RA. SIRI = (neutrophil count × monocyte count)/lymphocyte count. We found a nonlinear L-shaped relationship between SIRI and the risk of RA, with the range of low-risk SIRI levels being 1.1 to 2.29. This finding suggests that individuals falling outside the range have an increased risk of developing RA. The overall linear trend indicated that each unit increase in SIRI corresponded to a 5% increased risk of RA occurrence (95% confidence interval: 1.01-1.10), and this association was more pronounced in males. In U.S. adults, an L-shaped nonlinear relationship exists between the inflammatory biomarker SIRI and the risk of developing RA. These findings provide new insights for the early identification of RA disease onset.
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