JMIR Publications

ABSTRACT

Background:

Cardiovascular disease (CVD) risk among individuals with different body mass index (BMI) levels might depend on their metabolic health. It remains unclear to what extent metabolic health status and BMI affect CVD risk, either directly or through a mediator, in the Chinese population.

Objective:

In this study, the Bayesian Networks (BN) perspective was adopted to characterise the multivariable probabilistic connections between CVD risk and metabolic health and obesity status, and to identify potential factors that influence these relationships among Chinese adults.

Methods:

The study population consisted of 6276 Chinese adults aged 30-74 years who were participants in the China Health and Nutrition Survey 2009. BMI was used to categorise participants as normal weight, overweight, or obese, and metabolic health was defined by the Adult Treatment Panel (ATP)-III criteria. Subjects were categorised into six phenotypes according to their metabolic health and BMI categorisation. The 10-year risk of CVD was determined using the Framingham Risk Score. BN modelling was used to identify the network structure of the variables, and then to compute the conditional probability of CVD risk for the different metabolic obesity phenotypes with the given structure.

Results:

Among the total sample, 65%, 20% and 15% of the subjects had a low, moderate and high 10-year CVD risk, respectively, without variable instantiated in the BN model. An averaged BN with a stable network structure was constructed by learning 300 bootstrapped networks from the data. Using BN reasoning, the conditional probability of high-CVD risk increased as age progressed. The conditional probability of high CVD risk was 0.43% (95%, CI 0.2-0.87) for the 30-40 years age group, 2.25% (1.75-2.88) for the 40-50 years age group, 16.13% (14.86-17.5) for the 50-60 years age group, and 52.02% (47.62-56.38) for those aged ≥70 years. When metabolic health and BMI categories were instantiated to their different status, the conditional probability of high CVD risk increased from 7.01% (6.27-7.83) for metabolically healthy normal weight (MHNW) subjects to 10.47% (7.63-14.18) for their metabolically healthy obese (MHO) counterparts, and up to 21.74% and 34.48% among metabolically unhealthy normal weight (MUNW) and metabolically unhealthy obese (MUO) subjects, respectively. In addition, sex was a significant modifier of the conditional probability distribution of metabolic obesity phenotypes and high-CVD risk, with a conditional probability of high CVD risk of only 2.02% and 22.7% among MHO and MUO women, respectively, compared to 21.92% and 48.21% for their male MHO and MUO counterparts.

Conclusions:

BN modelling was applied to investigate the relationship between CVD risk and metabolic health and obesity phenotypes in Chinese adults. The results suggest that both metabolic health and obesity status are of importance for CVD prevention, and closer attention should be paid to BMI and metabolic status changes over time.