Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018
BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease marked by systemic inflammation and immune dysregulation, leading to a higher risk of all-cause mortality. The Pan-Immune Inflammation Value (PIV), a novel biomarker capturing immune-inflammatory activity, has shown prognostic value...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| author | Muradil· Mardan Huoliang Zheng Qingyin Xu Shaokuan Song Zeyu Lu Hui Deng Hao Cai Qizhu Chen Bingyi Yang Kudelaiti· Abuduwufuer Pengbo Chen Bo Li Shengdan Jiang Leisheng Jiang Xin-feng Zheng |
| author_facet | Muradil· Mardan Huoliang Zheng Qingyin Xu Shaokuan Song Zeyu Lu Hui Deng Hao Cai Qizhu Chen Bingyi Yang Kudelaiti· Abuduwufuer Pengbo Chen Bo Li Shengdan Jiang Leisheng Jiang Xin-feng Zheng |
| author_sort | Muradil· Mardan |
| collection | DOAJ |
| description | BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease marked by systemic inflammation and immune dysregulation, leading to a higher risk of all-cause mortality. The Pan-Immune Inflammation Value (PIV), a novel biomarker capturing immune-inflammatory activity, has shown prognostic value in various diseases. However, its role in predicting outcomes in RA patients remains largely unexplored.ObjectivesThis study aimed to evaluate the association between PIV and all-cause mortality in RA patients, investigate nonlinear relationships, and identify threshold effects.MethodsData from the 1999–2018 National Health and Nutrition Examination Survey (NHANES) were used, including 1,882 RA patients. PIV was calculated as (neutrophil count×platelet count×monocyte count)/lymphocyte count and categorized into quartiles (Q1–Q4). Multivariable Cox proportional hazards models were applied to assess the relationship between PIV and mortality, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Restricted cubic splines (RCS) explored nonlinear trends, and segmented Cox regression identified threshold effects. Kaplan-Meier survival curves and subgroup analyses validated the findings and assessed potential modifiers.ResultsElevated PIV levels were strongly associated with increased all-cause mortality. Compared to Q1, adjusted HRs for Q2, Q3, and Q4 were 1.60 (95% CI: 1.01–2.53, P = 0.047), 1.70 (95% CI: 1.10–2.63, P = 0.016), and 2.12 (95% CI: 1.33–3.37, P = 0.002), respectively (P for trend < 0.001). RCS analysis revealed a nonlinear relationship with a threshold at PIV = 302. Below this threshold, increasing PIV was associated with higher mortality risk (HR = 1.67, 95% CI: 1.07–2.61, P = 0.024). Conversely, above the threshold, further increases in PIV were linked to reduced mortality risk (HR = 0.98, 95% CI: 0.97–0.99, P = 0.026). Kaplan-Meier survival curves showed a clear decline in survival probability with increasing PIV quartiles (P < 0.001). Subgroup analyses confirmed consistent findings, with a notable interaction observed in diabetic patients (P for interaction = 0.002).ConclusionsPIV is a significant and independent predictor of all-cause mortality in RA patients, characterized by a nonlinear association and a distinct threshold effect. These findings highlight the potential of PIV as a pragmatic biomarker for stratifying mortality risk and informing personalized treatment strategies in RA. |
| format | Article |
| id | doaj-art-da28184a47a545d08b6d2a38655173cc |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-da28184a47a545d08b6d2a38655173cc2025-01-28T06:41:27ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011610.3389/fimmu.2025.15499551549955Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018Muradil· Mardan0Huoliang Zheng1Qingyin Xu2Shaokuan Song3Zeyu Lu4Hui Deng5Hao Cai6Qizhu Chen7Bingyi Yang8Kudelaiti· Abuduwufuer9Pengbo Chen10Bo Li11Shengdan Jiang12Leisheng Jiang13Xin-feng Zheng14Spine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Oncology, Shanghai Traditional Chinese Medicine Hospital, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaSpine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, ChinaBackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease marked by systemic inflammation and immune dysregulation, leading to a higher risk of all-cause mortality. The Pan-Immune Inflammation Value (PIV), a novel biomarker capturing immune-inflammatory activity, has shown prognostic value in various diseases. However, its role in predicting outcomes in RA patients remains largely unexplored.ObjectivesThis study aimed to evaluate the association between PIV and all-cause mortality in RA patients, investigate nonlinear relationships, and identify threshold effects.MethodsData from the 1999–2018 National Health and Nutrition Examination Survey (NHANES) were used, including 1,882 RA patients. PIV was calculated as (neutrophil count×platelet count×monocyte count)/lymphocyte count and categorized into quartiles (Q1–Q4). Multivariable Cox proportional hazards models were applied to assess the relationship between PIV and mortality, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Restricted cubic splines (RCS) explored nonlinear trends, and segmented Cox regression identified threshold effects. Kaplan-Meier survival curves and subgroup analyses validated the findings and assessed potential modifiers.ResultsElevated PIV levels were strongly associated with increased all-cause mortality. Compared to Q1, adjusted HRs for Q2, Q3, and Q4 were 1.60 (95% CI: 1.01–2.53, P = 0.047), 1.70 (95% CI: 1.10–2.63, P = 0.016), and 2.12 (95% CI: 1.33–3.37, P = 0.002), respectively (P for trend < 0.001). RCS analysis revealed a nonlinear relationship with a threshold at PIV = 302. Below this threshold, increasing PIV was associated with higher mortality risk (HR = 1.67, 95% CI: 1.07–2.61, P = 0.024). Conversely, above the threshold, further increases in PIV were linked to reduced mortality risk (HR = 0.98, 95% CI: 0.97–0.99, P = 0.026). Kaplan-Meier survival curves showed a clear decline in survival probability with increasing PIV quartiles (P < 0.001). Subgroup analyses confirmed consistent findings, with a notable interaction observed in diabetic patients (P for interaction = 0.002).ConclusionsPIV is a significant and independent predictor of all-cause mortality in RA patients, characterized by a nonlinear association and a distinct threshold effect. These findings highlight the potential of PIV as a pragmatic biomarker for stratifying mortality risk and informing personalized treatment strategies in RA.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1549955/fullrheumatoid arthritisPan-Immune Inflammation Valueall-cause mortalitysystemic inflammationprognostic biomarkerNHANES |
| spellingShingle | Muradil· Mardan Huoliang Zheng Qingyin Xu Shaokuan Song Zeyu Lu Hui Deng Hao Cai Qizhu Chen Bingyi Yang Kudelaiti· Abuduwufuer Pengbo Chen Bo Li Shengdan Jiang Leisheng Jiang Xin-feng Zheng Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018 Frontiers in Immunology rheumatoid arthritis Pan-Immune Inflammation Value all-cause mortality systemic inflammation prognostic biomarker NHANES |
| title | Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018 |
| title_full | Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018 |
| title_fullStr | Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018 |
| title_full_unstemmed | Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018 |
| title_short | Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018 |
| title_sort | comprehensive analysis of pan immune inflammation and all cause mortality in rheumatoid arthritis a database driven approach 1999 2018 |
| topic | rheumatoid arthritis Pan-Immune Inflammation Value all-cause mortality systemic inflammation prognostic biomarker NHANES |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1549955/full |
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