Evidence map›Paper›PMID 39574129›Full record

ArticleCardiovascular diabetology2024

Pathways from insulin resistance to incident cardiovascular disease: a Bayesian network analysis.

Xue Tian, Shuohua Chen, Xue Xia, Qin Xu, Yijun Zhang, Chenhao Zheng, Shouling Wu, Anxin Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Xue TianDepartment of Epidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Shuohua ChenDepartment of Cardiology, Kailuan Hospital, North China University of Science and Technology, 57 Xinhua East Rd, Tangshan, 063000, China.
Xue XiaDepartment of Epidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Qin XuDepartment of Epidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Yijun ZhangDepartment of Epidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Chenhao ZhengStatistics-Mathematics, Rutgers University, New Brunswick, New-Brunswick, NJ, USA.
Shouling WuDepartment of Cardiology, Kailuan Hospital, North China University of Science and Technology, 57 Xinhua East Rd, Tangshan, 063000, China. drwusl@163.com.
Anxin WangDepartment of Epidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China. wanganxin@bjtth.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInsulin resistance coexist with many metabolic disorders, whether these disorders were promotors or pathway-factors for the association of insulin resistance and cardiovascular disease (CVD) remained unclear. We aimed to investigate the pathways related to elevated the triglyceride-glucose (TyG) index and pathways through elevated TyG index to the occurrence of CVD in Chinese adults.

methodsA total of 96,506 participants were enrolled from the Kailuan study. Bayesian network model with the max-min hill climbing algorithm and maximum likelihood estimation was applied to identify factors and pathways related to the TyG index, and quantitatively infer the impact of associated factors on elevated TyG index and the occurrence of CVD by computing conditional probabilities.

resultsA final Bayesian network was constructed with 14 nodes and 25 arcs, creating 28 pathways related to elevated TyG index and 8 pathways from elevated TyG index to CVD. Elevated TyG index was causally associated with CVD, the condition probability was 11.9%. Pathways to elevated TyG index were mainly through unhealthy lifestyles and the subsequent increase in lipid profiles, especially smoking and low-density lipoprotein cholesterol. The most important pathway from elevated TyG index to CVD was through overweight/obesity, hypertension, and chronic kidney disease, with a condition probability of 18.5%. The maximum relative change rate related to elevated TyG index was observed for overweight/obesity (64.3%).

conclusionsElevated TyG index was causally associated with the risk of CVD, a combined control of lifestyles and metabolic factors may contribute to the reduction of TyG index and the prevention of CVD.

Indexed as

Bayes TheoremBiomarkersBlood GlucoseCardiovascular DiseasesInsulin ResistanceTriglyceridesAdultAgedChinaFemaleHeart Disease Risk FactorsHumansIncidenceMaleMiddle AgedObesityBiomarkersBlood GlucoseTriglyceridesBayesian network analysisCardiovascular diseaseCausal associationTriglyceride-glucose index

Identifiers

PMID39574129
PMCPMC11583553

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.