Evidence map›Paper›PMID 40537785›Full record

ArticleJournal of translational medicine2025

Identifying pathways to cardiovascular mortality by causal graphical models and mediation analysis among hypertensive patients: insights from a prospective study.

Simiao Tian, Zhen Li, Yanhong Bi, Xiaoyu Che, Ao Feng, Yiou Wang

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Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Simiao TianDepartment of Medical Records and Statistics, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China. tiansimiao@dlu.edu.cn.ORCID 0000-0002-6419-0718
Zhen LiDepartment of Medical Records and Statistics, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China.
Yanhong BiDepartment of Research, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Xiaoyu CheDepartment of Research, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Ao FengDepartment of Prevention and Healthcare, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Yiou WangDepartment of Medical Records and Statistics, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China. 727184278@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany biochemical markers are involved in cardiovascular (CV) prognosis in the hypertensive population, but most findings are derived from a single-exposure setting, and their interaction and potential pathways remain scarce. The aim of this study was to determine the direct cause-effect relationship and the mediating effect of CV mortality to suggest potential pathways.

methodsThis prospective study analysed a data from 3559 hypertensive individuals from the National Health and Nutrition Examination Survey (1999-2018), with their CV mortality ascertained through linkage to the National Death Index on December 31, 2019. Baseline sociodemographic characteristics, habits, medical history data and serum biochemical markers, including cardiometabolic markers, inflammatory markers, liver enzyme markers, blood-cell based inflammatory and immune markers and kidney and renal markers were recorded. The Mixed Graphical Model-Fast-Causal Inference-Maximum algorithm (MGM-FCI-MAX) was applied to build a causal graphical model (CGM) depicting direct and indirect causes of CV mortality, then pathways were further identified from CGM where mediation analyses were performed.

resultsOf the total participants, 562 (15.79%, 302 men and 260 women) CV deaths occurred after a median follow-up of 154 months. Survival analysis revealed significant sex- and ethnicity-specific differences in CV mortality rates (log-rank P < 0.01 and P < 0.01, respectively). Based on the resulting CGM, we identified three direct causes, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN) and monocytes, of CV mortality, representing direct pathways underlying kidney and renal function and blood-cell based inflammatory function, respectively. BUN significantly mediated 30.29% of the effect of the eGFR on CV mortality, whereas neither the liver enzyme markers nor insulin pathway with the eGFR as a mediator showed a significant tendency towards a mediated effect after adjusting for covariates. Sex and race were significantly (21.73% and 20.96%, respectively) mediated by monocytes and the eGFR for CV mortality.

conclusionBy using prospective survey data and background clinical knowledge, CGM retrieved direct and indirect causes of CV prognosis and identified pathways and the associated mediated effects. These insights will be useful in designing clinical protocols and targeting improvements in hypertensive patient management.

Indexed as

Cardiovascular DiseasesHypertensionMediation AnalysisAgedBiomarkersCausalityFemaleHumansMaleMiddle AgedProspective StudiesRisk FactorsSurvival AnalysisBiomarkersCardiovascular mortalityCausal graphical modelsHypertensionNHANES

Identifiers

PMID40537785
PMCPMC12180277

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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 Socratic graph.