Journal of Sasang Constitutional Medicine (사상체질의학회지)

The Society Of Sasang Constitutional Medicine (사상체질의학회)
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Clinically Significant Cut-off Value of the KS-15 for the Risk of Metabolic Syndrome: Using the Korean Medicine Daejeon Citizen Cohort (KDCC) Study

대전시민코호트 자료를 활용한 대사증후군에 임상적으로 유의한 KS-15의 사상체질 판별 기준

  • Eun Kyoung, Ahn (KM Data Division, Korea Institute of Oriental Medicine) ;
  • Siwoo, Lee (KM Data Division, Korea Institute of Oriental Medicine) ;
  • Ji-Eun, Park (KM Data Division, Korea Institute of Oriental Medicine)
  • 안은경 (한국한의학연구원 한의약데이터부) ;
  • 이시우 (한국한의학연구원 한의약데이터부) ;
  • 박지은 (한국한의학연구원 한의약데이터부)
  • Received : 2022.09.05
  • Accepted : 2022.09.27
  • Published : 2022.12.30
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Abstract

Objectives The purpose of this study is to propose a method to more specifically identify Sasang constitutional risk factors of metabolic syndromes by adjusting the cut-off value of Korea Sasang Constitutional Diagnostic Questionnaire (KS-15). Methods Data of 1997 participants in Korean medicine Daejeon Citizen Cohort study (KDCC) were analyzed. Metabolic syndrome was defined according to the NCEP-ATP III, lifestyle information, and hematologic information including KS-15 and demographic characteristics were used as covariates. Results The 179 subjects with metabolic syndrome accounted for 9.0% of the total. As a result of determining the Sasang constitution for the KS-15 response based on the cut-off values (approximate 0.33), 0.5, and 0.6 of the constitutional score, when performed at the 0.6 cut-off model, the odds ratio of TE was 2.46 which showed a statistically significantly higher risk than the borderline group. For the accuracy of the model and the Area under the curve (AUC), the model accuracy based on the original cut-off of the KS-15 was 0.902 and AUC was 0.737. The accuracy of the model with cut-off of 0.5 and with of 0.6 were 0.904 and 0.902, respectively, and the AUCs were 0.687 and 0.741, respectively. Conclusion In this study, we confirmed that it is effective to increase the cut-off value of KS-15 to 0.6 in the metabolic syndrome risk model. It is expected that this could increase the accuracy of identifying high-risk groups for metabolic syndrome.

Keywords

Acknowledgement

본 연구의 한국한의학연구원 연구과제 '빅데이터 기반 한의 예방치료원천기술개발' (KSN2023120)의 일환으로 수행되었음.

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