Using Chinese Body Constitution Concepts and Measurable Variables for Assessing Risk of Coronary Artery Disease

Identifying patients with high risk of coronary artery disease (CAD) is often difficult in outpatient clinic settings. This study aimed to explore if the measurement of body constitution can be adopted to predict the risk of CAD diagnosis. The objective of this study is to conduct a prospective observational study and a case-control study to answer the research question.

Part 1 (prospective observational study): a total of 143 patients with chest pain and admitted to receive cardiac catheterization were enrolled, and 108 of them were diagnosed with CAD. Part 2 (case-control study): the above 108 CAD patients and 476 healthy controls matched by age and gender from the participants of Taiwan Biobank were adopted for comparison.

The body constitution of both patients and healthy controls were measured by the Body Constitution Questionnaire (BCQ). Each one received scores of Yang-Xu (Yang-deficiency), Yin-Xu (Yin-deficiency), and Stasis. These 3 scores together with demographic characteristics and CAD risk factors were used in the logistic multiple regression model to predict the risk of CAD.

(Part 1) No difference was found between the scores of Yang-Xu, Yin-Xu, and Stasis between the patients with and without CAD. (Part 2) The scores of Yang-Xu, Yin-Xu, and Stasis of the CAD patients were significant higher those of the healthy controls. Yang-Xu and Stasis scores were obtained with age, BMI, and hypertension in the model with prediction rate 89.0%. The area under receiver operating characteristic curve of this model was 0.896.

This study is the first to apply Chinese body constitution concepts and measurable variables to assess the risk of having CAD of the patients with chest pain prior to receiving cardiac catheterization. The higher scores of Yang-Xu and Stasis were found to be risk factors. Our results revealed that BCQ has the potential to be a first-line diagnostic tool for patients with chest pain to facilitate early recognition and diagnosis of CAD.