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ArticleCardiovascular toxicology2026

Association Between Blood Heavy Metals and Selenium with All-Cause Mortality in Cardiovascular-Kidney-Metabolic Syndrome Populations: Exploring the Mediating Effects of Inflammatory Biomarkers.

Hui Niu, Lin Zhang, Yikun Wang, Wenchang He, Rui Wang, Xinhong Li

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Article in Cardiovascular toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Hui Niu *Department of Disease Prevention and Control, Lintong Rehabilitation and Recuperation Centre, Xi'an, 710600, Shaanxi, China.
Lin Zhang *Department of Internal Medicine, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, Shaanxi, China.
Yikun WangDepartment of General Medicine, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, Shaanxi, China.
Wenchang HeDepartment of General Medicine, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, Shaanxi, China.
Rui WangDepartment of General Medicine, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, Shaanxi, China.
Xinhong LiDepartment of General Medicine, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, Shaanxi, China. 2818413616@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular-kidney-metabolic (CKM) syndrome, characterized by the pathophysiological interplay among metabolic disorders, chronic kidney disease (CKD), and cardiovascular disease (CVD), significantly elevates mortality risk. Environmental heavy metal exposure and selenium deficiency are implicated in these conditions, but their combined impact on CKM syndrome is unclear. To investigate the associations of blood heavy metals (lead [Pb], cadmium [Cd], mercury [Hg], manganese [Mn]) and selenium (Se) with all-cause mortality in CKM populations, and examine the mediating role of inflammatory biomarkers. This cross-sectional study with prospective mortality follow-up included 6,072 participants from NHANES (2011-2018). Kaplan-Meier curves, multivariable Cox regression models, and restricted cubic spline (RCS) analyses were employed to assess all-cause mortality associations. Subgroup and interaction analyses evaluated risks across demographic strata. Mediation analysis was employed to explore the mediating effects of inflammatory biomarkers (NLR, MLR, NMLR, and SIRI). The Weighted quantile sum (WQS) model was utilized to estimate the effects of combined blood metal exposures. Among 6,072 participants, 409 deaths occurred during follow-up. In the fully adjusted model, there was a significant negative correlation between blood selenium levels and all-cause mortality in the CKM population. Compared with the lowest quartile (Q1), the highest selenium quartile (Q4) was associated with a 38% reduced mortality risk (HR = 0.62, 95% CI: 0.45-0.85, P = 0.003). RCS analysis revealed an L-shaped dose-response relationship (P for nonlinear = 0.002). Subgroup analyses confirmed consistent associations in both non-advanced CKM (stages 0-2) and advanced CKM (stages 3-4) (all P < 0.05, P for interaction = 0.163). Mediation analysis revealed that NLR, MLR, NMLR, and SIRI partially mediated the association between blood selenium and all-cause mortality, and the mediated proportions were relatively modest (ranging from 3.57 to 5.29%). Higher blood selenium was associated with reduced all-cause mortality in CKM syndrome after adjustment for measured confounders, and mediation analysis suggested a potential partial role of inflammation in this association. These findings underscore the need for targeted interventions to mitigate mortality in this high-risk population.

Indexed as

Cardio-Renal SyndromeCardiovascular DiseasesInflammationInflammation MediatorsMetabolic SyndromeMetals, HeavySeleniumAdultAgedBiomarkersCause of DeathCross-Sectional StudiesFemaleHumansMaleMiddle AgedBiomarkersInflammation MediatorsMetals, HeavySeleniumAll-cause mortalityBlood seleniumCardiovascular-kidney-metabolic syndromeInflammation biomarkers

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.