ArticleFrontiers in public health2022
Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Prediction models for progression from prediabetes to diabetes: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Sofrito and Fruit Consumption Associated with Lower Risk of Type 2 Diabetes in an Urban Latin American Cohort.Nutrients · 2026Article
- Risk factors and interrelationships of taxane-related peripheral neuropathy: a Bayesian network analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Ultra-processed food intake is associated with altered glucose homeostasis in young adults with a history of overweight or obesity: a longitudinal study.Nutrition & metabolism · 2025Article
- Bayesian network model of ethno-racial disparities in cardiometabolic-based chronic disease using NHANES 1999-2018.Frontiers in public health · 2024Article
- Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network.PloS one · 2024Article
- A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus.BMC medical research methodology · 2023Article
- Enhancing stroke-associated pneumonia prediction in ischemic stroke: An interpretable Bayesian network approach.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: It is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease. Methods: This study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Results: The BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to Conclusion: The feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.
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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.