Evidence map›Paper›PMID 42152009›Full record

ArticleBMC medical informatics and decision making2026

Causal Bayesian network learning: application to the causal analysis of masticatory function and coronary heart disease in older adults.

Chunzi Wang, Yunwei Zhang, Chenghao Qiu, Lingshan Wan, Yifan Cao, Shuangcheng Wang, Wendi Cheng, Hansheng Ding

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Chunzi Wang *Department of Fintech, Shanghai Normal University Tianhua College, No. 1661 Shengxin North Road, Jiading District, Shanghai, 201815, P.R. China.
Yunwei Zhang *Shanghai Health Development Research Center (Shanghai Medical Information Center), No. 602 West Jianguo Road, Shanghai, 200031, P.R. China.
Chenghao Qiu *Department of Stomatology, Shanghai Public Health Clinical Center, No. 2901, Caolang Road, Jinshan District, Shanghai, P.R. China.
Lingshan WanShanghai Health Development Research Center (Shanghai Medical Information Center), No. 602 West Jianguo Road, Shanghai, 200031, P.R. China.
Yifan CaoShanghai Health Development Research Center (Shanghai Medical Information Center), No. 602 West Jianguo Road, Shanghai, 200031, P.R. China.
Shuangcheng WangDepartment of Fintech, Shanghai Normal University Tianhua College, No. 1661 Shengxin North Road, Jiading District, Shanghai, 201815, P.R. China.
Wendi ChengShanghai Health Development Research Center (Shanghai Medical Information Center), No. 602 West Jianguo Road, Shanghai, 200031, P.R. China.
Hansheng DingShanghai Health Development Research Center (Shanghai Medical Information Center), No. 602 West Jianguo Road, Shanghai, 200031, P.R. China. dinghansheng@shdrc.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronary heart disease (CHD) is a leading global cause of death, with established links to various risk factors. Recent evidence suggests an association between impaired masticatory function and CHD, but the causal nature of this relationship remains unclear.

methodsTo investigate the causal relationship between masticatory function and CHD, we propose a novel hybrid approach that combined constraint-based and score-based methods to learn causal Bayesian network (CBN) structures from observational data. The proposed method is compared against PC, TAN, Naive Bayes, Chow-Liu, and HillClimb algorithms using BIC, K2, BDeu, and BDs scores. A cross-sectional study was conducted in Shanghai, China, from January to May 2023. Data were collected via structured questionnaires, yielding a sample of 179,141 individuals. For analysis, 10,817 healthy individuals (free of any chronic diseases) and 3,382 individuals with CHD are selected. A total of 15 variables covering sociodemographic, functional, cognitive, and health-related aspects are included in the CBN modeling to capture relevant confounders and mediators.

resultsThe proposed method outperforms several baseline algorithms while maintaining model interpretability. The learned CBN identifies age, motor function, medical constipation, and sleep as direct causes of CHD. Masticatory function is found to influence CHD indirectly through its effects on motor function and medical constipation. Probabilistic inference indicates that sleep, muscle force, cognitive function and education level are key confounders, with decreasing levels of confounding impact. Causal inference using do-calculus reveals that abnormal masticatory function increases CHD risk by 23% points after adjusting for confounding impact, compared to 39% points without adjustment.

conclusionsThis hybrid CBN approach effectively uncovers interpretable causal pathways, emphasizing the role of oral health in cardiovascular risk and demonstrating the utility of causal models in informing public health strategies.

Indexed as

Coronary DiseaseMachine LearningMasticationAgedBayes TheoremChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedCausal Bayesian networkCausal effectCoronary heart diseaseMasticatory function

Identifiers

PMID42152009
PMCPMC13352697

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.