Inflammatory Cytokines Associated with Apparent Treatment-Resistant Hypertension in the Maintenance Hemodialysis Population: Insights from Machine Learning

Background: The prevalence of apparent treatment-resistant hypertension (aTRH) among individuals undergoing maintenance hemodialysis (MHD) ranges from 18% to 42%, significantly increasing cardiovascular risks and mortality. Chronic inflammation may contribute to resistant hypertension; however, the specific inflammatory cytokine profiles associated with aTRH in the dialysis population remain unclear. Methods: The discovery cohort included 78 participants from the CARE-MHD study (February–May 2022), while the validation cohort consisted of 168 participants from the PURE-HD study (October 2023–April 2024). Twenty-seven cytokines were measured using a multiplex immunoassay (discovery) and validated by ELISA (validation). Participants were classified into aTRH, non-aTRH hypertension, and normotension groups. Logistic regression (LR) and machine learning (ML) models assessed cytokine associations and predictive capabilities. Results: Among the analyzed cytokines, IL-4, IL-9, Eotaxin, and RANTES were significantly elevated in aTRH participants compared to both non-aTRH hypertensive and normotensive groups. An LR model incorporating all four cytokines exhibited the best variance explanation ability (adjusted Cox and Snell R2 = 0.411) among other LR models, confirming the association between these cytokines and aTRH. LR analyses demonstrated the strong predictive potential of RANTES and Eotaxin. A multivariate model incorporating all four cytokines showed superior predictive performance. ML models consistently identified RANTES and Eotaxin as critical predictive biomarkers for aTRH. Additionally, protein-protein interaction network analysis revealed that all four cytokines are involved in inflammatory pathways linked to vascular dysfunction and immune cell activation. Conclusion: A distinct inflammatory signature (IL-4, IL-9, Eotaxin, RANTES) is associated with aTRH in the MHD population. Among these, RANTES and Eotaxin may serve as non-invasive biomarkers for early diagnosis and personalized therapeutic strategies in this high-risk population. Funding: Government Support – Non-U.S.

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Journal
Journal of the American Society of Nephrology
Published
2025-10-01
DOI
https://doi.org/10.1681/asn.2025ynsedd28
Primary Topic
Artificial Intelligence in Healthcare
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article
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article

Inflammatory Cytokines Associated with Apparent Treatment-Resistant Hypertension in the Maintenance Hemodialysis Population: Insights from Machine Learning

Chunsun Dai, Ye Hong, Yuan Luo
Journal of the American Society of Nephrology
Artificial Intelligence in Healthcare
article

Inflammatory Cytokines Associated with Apparent Treatment-Resistant Hypertension in the Maintenance Hemodialysis Population: Insights from Machine Learning

Chunsun Dai, Ye Hong, Yuan Luo
article en

Abstract

Background: The prevalence of apparent treatment-resistant hypertension (aTRH) among individuals undergoing maintenance hemodialysis (MHD) ranges from 18% to 42%, significantly increasing cardiovascular risks and mortality. Chronic inflammation may contribute to resistant hypertension; however, the specific inflammatory cytokine profiles associated with aTRH in the dialysis population remain unclear. Methods: The discovery cohort included 78 participants from the CARE-MHD study (February–May 2022), while the validation cohort consisted of 168 participants from the PURE-HD study (October 2023–April 2024). Twenty-seven cytokines were measured using a multiplex immunoassay (discovery) and validated by ELISA (validation). Participants were classified into aTRH, non-aTRH hypertension, and normotension groups. Logistic regression (LR) and machine learning (ML) models assessed cytokine associations and predictive capabilities. Results: Among the analyzed cytokines, IL-4, IL-9, Eotaxin, and RANTES were significantly elevated in aTRH participants compared to both non-aTRH hypertensive and normotensive groups. An LR model incorporating all four cytokines exhibited the best variance explanation ability (adjusted Cox and Snell R2 = 0.411) among other LR models, confirming the association between these cytokines and aTRH. LR analyses demonstrated the strong predictive potential of RANTES and Eotaxin. A multivariate model incorporating all four cytokines showed superior predictive performance. ML models consistently identified RANTES and Eotaxin as critical predictive biomarkers for aTRH. Additionally, protein-protein interaction network analysis revealed that all four cytokines are involved in inflammatory pathways linked to vascular dysfunction and immune cell activation. Conclusion: A distinct inflammatory signature (IL-4, IL-9, Eotaxin, RANTES) is associated with aTRH in the MHD population. Among these, RANTES and Eotaxin may serve as non-invasive biomarkers for early diagnosis and personalized therapeutic strategies in this high-risk population. Funding: Government Support – Non-U.S.

Journal of the American Society of NephrologyVol. 36(10S)
Second Affiliated Hospital of Nanjing Medical University (CN)
Openalex Percentile: Top 66%
Artificial Intelligence in Healthcare
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