Dataset Information

A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study.

ABSTRACT:

Background

Existing diabetes risk prediction models based on regression were limited in dealing with collinearity and complex interactions. Bayesian network (BN) model that considers interactions may provide additional information to predict risk and infer causation.

Methods

BN model was constructed for new-onset diabetes using prospective data of 15,934 participants without diabetes at baseline [73% women; mean (standard deviation) age = 61.0 (6.9) years]. Participants were randomly assigned to a training (n = 12,748) set and a validation (n = 3,186) set. Model performances were assessed using area under the receiver operating characteristic curve (AUC).

Results

During an average follow-up of 4.1 (interquartile range = 3.3-4.5) years, 1,302 (8.17%) participants developed diabetes. The constructed BN model showed the associations (direct, indirect, or no) among 24 risk factors, and only hypertension, impaired fasting glucose (IFG; fasting glucose of 5.6-6.9 mmol/L), and greater waist circumference (WC) were directly associated with new-onset diabetes. The risk prediction model showed that the post-test probability of developing diabetes in participants with hypertension, IFG, and greater WC was 27.5%, with AUC of 0.746 [95% confidence interval CI) = 0.732-0.760], sensitivity of 0.727 (95% CI = 0.703-0.752), and specificity of 0.660 (95% CI = 0.652-0.667). This prediction model appeared to perform better than a logistic regression model using the same three predictors (AUC = 0.734, 95% CI = 0.703-0.764, sensitivity = 0.604, and specificity = 0.745).

Conclusions

We have first reported a BN model in predicting new-onset diabetes with the smallest number of factors among existing models in the literature. BN yielded a more comprehensive figure showing graphically the inter-relations for multiple factors with diabetes than existing regression models.

SUBMITTER: Wang Y

PROVIDER: S-EPMC9382298 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study.

Wang Ying Y Zhang Wei Sen WS Hao Yuan Tao YT Jiang Chao Qiang CQ Jin Ya Li YL Cheng Kar Keung KK Lam Tai Hing TH Xu Lin L

Frontiers in endocrinology 20220803

<h4>Background</h4>Existing diabetes risk prediction models based on regression were limited in dealing with collinearity and complex interactions. Bayesian network (BN) model that considers interactions may provide additional information to predict risk and infer causation.<h4>Methods</h4>BN model was constructed for new-onset diabetes using prospective data of 15,934 participants without diabetes at baseline [73% women; mean (standard deviation) age = 61.0 (6.9) years]. Participants were rando ...[more]

PMID: 35992128

Similar Datasets

Project description:BackgroundPhysical activity (PA) is generally encouraged. Studies from developed countries in the West have shown that maintenance of adequate PA or increasing PA are associated with lower mortality risk. It is unclear whether these associations apply to an older Chinese population. Hence, we examined the changes in PA prospectively among a middle-aged and older Chinese population over an average of 4 years and explored their subsequent mortality risks.MethodsMetabolic equivalent scores of PA among participants in the Guangzhou Biobank Cohort Study were calculated. Participants were divided into 3 groups related to PA level, and changes in PA were classified into 9 categories. Information on vital status and causes of death from March 2008 to December 2012 (the first repeated examination) until December 31, 2017, was obtained via record linkage with the Death Registry.ResultsOf 18,104 participants aged 61.21 ± 6.85 years (mean ± SD), 1461 deaths occurred within 141,417 person-years. Compared to participants who maintained moderate PA, those who decreased PA from moderate or high levels to a low level had increased risks for all-cause mortality (hazard ratio (HR) = 1.47, 95% confidence interval (95%CI): 1.11-1.96). Participants who maintained a high level of PA (HR = 0.83, 95%CI: 0.70-0.98) or increased PA from low to high levels (HR = 0.71, 95%CI: 0.52-0.97) showed lower all-cause mortality risks. Those who maintained low PA levels showed a higher all-cause mortality risk, whereas those who increased their PA levels showed a non-significantly lower risk. Similar results were found for cardiovascular disease risk.ConclusionEven at an older age, maintaining a high PA level or increasing PA from low to high levels results in lower mortality risks, suggesting that substantial health benefits might be achieved by maintaining or increasing engagement in adequate levels of PA. The increased risk of maintaining a low PA level or decreasing PA to a low level warrants the attention of public health officials and clinicians.

Dataset Information

A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study.

Background

Methods

Results

Conclusions

Publications

A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study.

Similar Datasets

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